use uint8 dtype for qlora

fix error with lambda and pickling

make sure things are in float instead of np.float

.github/workflows/base.yml

@@ -22,12 +22,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.4.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""

.github/workflows/main.yml

@@ -15,16 +15,11 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.5.1
-            axolotl_extras: vllm
+            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -35,7 +30,7 @@ jobs:
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.0
-            axolotl_extras: vllm
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -67,6 +62,7 @@ jobs:
             CUDA=${{ matrix.cuda }}
             PYTORCH_VERSION=${{ matrix.pytorch }}
             AXOLOTL_ARGS=${{ matrix.axolotl_args }}
+            AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}
           file: ./docker/Dockerfile
           push: ${{ github.event_name != 'pull_request' }}
           tags: |
@@ -82,11 +78,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"

.github/workflows/multi-gpu-e2e.yml

@@ -9,6 +9,7 @@ on:
       - 'pyproject.toml'
       - '.github/workflows/multi-gpu-e2e.yml'
       - 'src/axolotl/core/trainers/mixins/sequence_parallel.py'
+      - 'src/axolotl/utils/distributed.py'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday
@@ -32,18 +33,11 @@ jobs:
             axolotl_extras: vllm
             num_gpus: 2
             nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:  # no vllm support for 2.4.1
-            num_gpus: 2
-            nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.5.1
-            axolotl_extras: vllm
+            axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
           - cuda: 126

.github/workflows/nightlies.yml

@@ -12,11 +12,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -70,11 +65,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"

.github/workflows/preview-docs.yml

@@ -0,0 +1,61 @@
+name: Preview
+on:
+  workflow_dispatch:
+  pull_request:
+    types: [opened, synchronize, reopened]
+
+    # Run the workflow only when one of these files changes
+    paths:
+      - '**/*.md'      # any Markdown file
+      - '**/*.qmd'     # any Quarto file
+      - '_quarto.yaml'
+
+permissions:
+  checks: write
+  contents: write
+  deployments: write
+  issues: write
+  discussions: write
+  pages: write
+  pull-requests: write
+  statuses: write
+
+jobs:
+  preview:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+
+      - name: Set up Quarto
+        uses: quarto-dev/quarto-actions/setup@v2
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Install dependencies
+        run: |
+          python3 -m pip install jupyter quartodoc
+          python3 -m pip install -e . --no-deps
+
+      - name: Build autodoc
+        run: quartodoc build
+
+      - name: Quarto render
+        run: quarto render
+
+      - name: Netlify Publish
+        uses: nwtgck/actions-netlify@v3.0
+        with:
+          publish-dir: './_site'
+          enable-pull-request-comment: true
+          enable-github-deployment: true
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          deploy-message: "Deployed On Netlify"
+          github-deployment-environment: 'preview'
+          github-deployment-description: 'Preview Deployment'
+        env:
+          NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
+          NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}

.github/workflows/tests-nightly.yml

@@ -26,7 +26,7 @@ jobs:
       max-parallel: 2
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
+        pytorch_version: ["2.5.1", "2.6.0", "2.7.0"]
     timeout-minutes: 20
 
     steps:
@@ -106,13 +106,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            num_gpus: 1
-            axolotl_extras:
-            nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"

.github/workflows/tests.yml

@@ -27,6 +27,9 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
 
+env:
+  TRANSFORMERS_IS_CI: "yes"
+
 jobs:
   pre-commit:
     name: pre-commit
@@ -49,7 +52,7 @@ jobs:
       max-parallel: 2
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0", "2.7.0"]
+        pytorch_version: ["2.5.1", "2.6.0", "2.7.0"]
     timeout-minutes: 20
 
     steps:
@@ -135,7 +138,7 @@ jobs:
       max-parallel: 1
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
+        pytorch_version: ["2.5.1", "2.6.0", "2.7.0"]
     timeout-minutes: 20
 
     steps:
@@ -258,6 +261,12 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.6.0
+            num_gpus: 1
+            axolotl_extras: llmcompressor
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -269,7 +278,7 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             num_gpus: 1
-            axolotl_extras: vllm
+            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"

.runpod/Dockerfile

@@ -1,11 +1,10 @@
-FROM runpod/pytorch:3.10-2.0.0-117
+FROM axolotlai/axolotl-cloud:main-py3.11-cu124-2.6.0
 
 COPY .runpod/requirements.txt /requirements.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
     python3 -m pip install --upgrade pip && \
     python3 -m pip install --upgrade -r /requirements.txt
 
-
 # Environment settings
 ARG BASE_VOLUME="/runpod-volume"
 ENV BASE_VOLUME=$BASE_VOLUME
@@ -15,4 +14,5 @@ ENV TRANSFORMERS_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
 
 COPY .runpod/src /src
 
+WORKDIR /src
 CMD ["python3", "/src/handler.py"]

.runpod/requirements.txt

@@ -5,11 +5,3 @@
 # git+https://github.com/runpod/runpod-python.git
 # To learn more, see https://pip.pypa.io/en/stable/reference/requirements-file-format/
 runpod~=1.7.0
-huggingface_hub
-typing-extensions
-pydantic
-pydantic-settings
-hf-transfer
-setuptools
-numpy==2.0.0
-axolotl[flash-attn,deepspeed]

.runpod/test-input.json

@@ -0,0 +1,86 @@
+{
+  "input": {
+    "name": "quick_smoke_test_sft",
+    "user_id": "user",
+    "model_id": "llama-test",
+    "run_id": "llama-test",
+    "credentials": {
+      "wandb_api_key": "",
+      "hf_token": ""
+    },
+    "args": {
+      "base_model": "HuggingFaceTB/SmolLM2-135M",
+      "model_type": "AutoModelForCausalLM",
+      "tokenizer_type": "AutoTokenizer",
+      "load_in_4bit": true,
+      "strict": false,
+      "datasets": [
+        {
+          "path": "mhenrichsen/alpaca_2k_test",
+          "type": "alpaca",
+          "split": "train[:10%]"
+        }
+      ],
+      "val_set_size": 0.02,
+      "output_dir": "./outputs/lora-out",
+      "sequence_len": 4096,
+      "sample_packing": true,
+      "eval_sample_packing": false,
+      "pad_to_sequence_len": true,
+      "adapter": "qlora",
+      "lora_r": 32,
+      "lora_alpha": 64,
+      "lora_dropout": 0.05,
+      "lora_target_linear": true,
+      "lora_modules_to_save": [
+        "embed_tokens",
+        "lm_head"
+      ],
+      "gradient_accumulation_steps": 2,
+      "micro_batch_size": 1,
+      "num_epochs": 1,
+      "optimizer": "adamw_torch_fused",
+      "lr_scheduler": "cosine",
+      "learning_rate": 0.0002,
+      "train_on_inputs": false,
+      "group_by_length": false,
+      "bf16": "auto",
+      "tf32": true,
+      "gradient_checkpointing": true,
+      "logging_steps": 1,
+      "flash_attention": true,
+      "warmup_steps": 1,
+      "evals_per_epoch": 1,
+      "eval_max_new_tokens": 128,
+      "saves_per_epoch": 1,
+      "weight_decay": 0.0,
+      "special_tokens": {
+        "pad_token": "<|endoftext|>"
+      },
+      "max_steps": 20
+    },
+    "timeout": 100000
+  },
+  "config": {
+    "gpuTypeId": "NVIDIA GeForce RTX 4090",
+    "gpuCount": 1,
+    "containerDiskInGb": 200,
+    "env": [
+      {
+        "key": "TOKENIZER",
+        "value": ""
+      },
+      {
+        "key": "DISABLE_LOG_STATS",
+        "value": "true"
+      }
+    ],
+    "allowedCudaVersions": [
+      "12.8",
+      "12.7",
+      "12.6",
+      "12.5",
+      "12.4"
+    ]
+  }
+}

.runpod/tests.json

@@ -11,43 +11,43 @@
           "hf_token": ""
         },
         "args": {
-          "base_model": "NousResearch/Meta-Llama-3-8B",
-          "model_type": "LlamaForCausalLM",
+          "base_model": "HuggingFaceTB/SmolLM2-135M",
+          "model_type": "AutoModelForCausalLM",
           "tokenizer_type": "AutoTokenizer",
-          "load_in_8bit": true,
-          "load_in_4bit": false,
+          "load_in_4bit": true,
           "strict": false,
           "datasets": [
             {
               "path": "mhenrichsen/alpaca_2k_test",
-              "type": "alpaca"
+              "type": "alpaca",
+              "split": "train[:10%]"
             }
           ],
-          "val_set_size": 0.05,
+          "val_set_size": 0.02,
           "output_dir": "./outputs/lora-out",
           "sequence_len": 4096,
           "sample_packing": true,
           "eval_sample_packing": false,
           "pad_to_sequence_len": true,
-          "adapter": "lora",
+          "adapter": "qlora",
           "lora_r": 32,
-          "lora_alpha": 16,
+          "lora_alpha": 64,
           "lora_dropout": 0.05,
           "lora_target_linear": true,
           "lora_modules_to_save": [
             "embed_tokens",
             "lm_head"
           ],
-          "gradient_accumulation_steps": 4,
-          "micro_batch_size": 2,
+          "gradient_accumulation_steps": 2,
+          "micro_batch_size": 1,
           "num_epochs": 1,
-          "optimizer": "adamw_bnb_8bit",
+          "optimizer": "adamw_torch_fused",
           "lr_scheduler": "cosine",
           "learning_rate": 0.0002,
           "train_on_inputs": false,
           "group_by_length": false,
           "bf16": "auto",
-          "tf32": false,
+          "tf32": true,
           "gradient_checkpointing": true,
           "logging_steps": 1,
           "flash_attention": true,
@@ -57,8 +57,9 @@
           "saves_per_epoch": 1,
           "weight_decay": 0.0,
           "special_tokens": {
-            "pad_token": "<|end_of_text|>"
-          }
+            "pad_token": "<|endoftext|>"
+          },
+          "max_steps": 20
         }
       },
       "timeout": 100000

cicd/multigpu.sh

@@ -20,4 +20,4 @@ pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/patched/ \
   --cov-report=xml:multigpu-coverage.xml
 
 # Upload coverage to Codecov
-codecov upload-process -t $CODECOV_TOKEN -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION}
+codecov upload-process -t "${CODECOV_TOKEN}" -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION} || true

docs/config.qmd

@@ -154,6 +154,10 @@ datasets:
     # Key containing the messages (default: "messages")
     field_messages: messages
 
+    # Key containing the system message (default: "system")
+    # If the system message is not present in the dataset sample, it will be loaded from the field_system property.
+    field_system: system
+
     # Mapping of properties from the input dataset to the chat template.
     # (default: message_property_mappings={'role':'role', 'content':'content'})
     # If a property exists in the template but not in this mapping, the system will attempt
@@ -180,10 +184,14 @@ datasets:
     # adding a system turn with empty content.
     drop_system_message:
 
+    # Optional[bool]. Whether to split the assistant turn based on a reasoning trace inside delimited tags
+    # defaults to False
+    split_thinking:
+
     # IMPORTANT: The following fields determine which parts of the conversation to train on.
     # Priority order: message_field_training > message_field_training_detail > train_on_inputs or role in roles_to_train
     # See examples at `docs/dataset-formats/conversation.qmd`
-    # Note: If the below 4 fields are set to empty, defaults to training only on the last message.
+    # Note: If the below 5 fields are empty, defaults to training only on the last message.
 
     # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
     roles_to_train: ["assistant"]  # default
@@ -192,7 +200,13 @@ datasets:
     # - turn (default): train on the EOS token at the end of each trainable turn
     # - last: train on the last EOS token in the conversation
     # TIP: Please make sure that your `tokenizer.eos_token` is same as EOS/EOT token in template. Otherwise, set `eos_token` under `special_tokens`.
-    train_on_eos: last
+    train_on_eos: turn
+    # Optional[str]. Which EOT (End-of-Turn) tokens to train on in the conversation. Possible values are:
+    # - all: train on all EOT tokens
+    # - turn: train on the EOT token at the end of each trainable turn
+    # - last: train on the last EOT token in the conversation
+    # If not specified, defaults to the value of train_on_eos for backward compatibility.
+    train_on_eot:
     # The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
     message_field_training: training
     # The key in the message turn that contains the training details. Useful to selectively train on certain tokens in a turn.
@@ -275,8 +289,17 @@ process_reward_model:
 chat_template: tokenizer_default
 # custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
 chat_template_jinja: null
-# Changes the default system message. Currently only supports chatml.
-default_system_message: You are a helpful assistant. Please give a long and detailed answer.
+# Optional[List[str]]. Custom EOT (End-of-Turn) tokens to mask/unmask during training.
+# These tokens mark the boundaries between conversation turns.
+# For example: ["/INST", "</s>", "[/SYSTEM_PROMPT]"]
+# If not specified, defaults to just the model's eos_token.
+# This is useful for templates that use multiple delimiter tokens.
+eot_tokens:
+  # - "</s>"
+  # - "[/INST]"
+  # - "[/SYSTEM_PROMPT]"
+# Changes the default system message
+default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared
@@ -661,8 +684,10 @@ special_tokens:
   # unk_token: "<unk>"
   # pad_token: "[PAD]"
 
-# Add extra tokens.
+# Optional[list[str]]. Add extra tokens to the tokenizer.
 tokens:
+  # - "<|startoftext|>"
+  # - "<|endoftext|>"
 
 # Mapping token_id to new_token_string to override reserved added_tokens in the tokenizer.
 # Only works for tokens that are not part of the base vocab (aka are added_tokens).

docs/custom_integrations.qmd

@@ -49,7 +49,8 @@ sections = [
     ("Knowledge Distillation (KD)", "kd"),
     ("Liger Kernels", "liger"),
     ("Language Model Evaluation Harness (LM Eval)", "lm_eval"),
-    ("Spectrum", "spectrum")
+    ("Spectrum", "spectrum"),
+    ("LLMCompressor", "llm_compressor")
 ]
 
 for section_name, folder_name in sections:

docs/dataset-formats/conversation.qmd

@@ -4,18 +4,6 @@ description: Conversation format for supervised fine-tuning.
 order: 3
 ---
 
-## sharegpt
-
-::: {.callout-important}
-ShareGPT is deprecated!. Please see [chat_template](#chat_template) section below.
-:::
-
-## pygmalion
-
-```{.json filename="data.jsonl"}
-{"conversations": [{"role": "...", "value": "..."}]}
-```
-
 ## chat_template
 
 Chat Template strategy uses a jinja2 template that converts a list of messages into a prompt. Support using tokenizer's template, a supported template, or custom jinja2.
@@ -64,7 +52,7 @@ We recommend checking the below examples for other usecases.
 
 ### Examples
 
-1. Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
+1. (Legacy) Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
 
 ```yaml
 datasets:
@@ -109,10 +97,55 @@ datasets:
 ```
 
 ::: {.callout-important}
-Please make sure that your `tokenizer.eos_token` is same as EOS/EOT token in template. Otherwise, set `eos_token` under `special_tokens`.
+Please make sure that your `tokenizer.eos_token` is same as EOS (End-of-Sequence) token in template. Otherwise, set `eos_token` under `special_tokens: `.
 :::
 
-5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
+5. If you are using a template that has a different EOT (End-of-Turn) token from EOS token or multiple EOT tokens (like Mistral V7 Tekken), set the `eot_tokens: ` config. The handling of EOT tokens follows `train_on_eos: ` which defaults to turn.
+
+```yaml
+eot_tokens:
+  - "[/INST]"
+  # - "[/SYSTEM_PROMPT]"
+
+datasets:
+  - path: ...
+    type: chat_template
+
+    # optional
+    train_on_eot: turn  # defaults read from train_on_eos (which defaults to turn)
+```
+
+::: {.callout-tip}
+See [config documentation](../config.qmd) for detailed explanations of "turn", "last", and "all" options for training on tokens.
+:::
+
+::: {.callout-note}
+Using `eot_tokens` requires each token that exists in `chat_template` to be a single token in the tokenizer. Otherwise, the tokenizer will split the token and cause unexpected behavior.
+
+You can add those tokens as new tokens under `tokens: ` or (recommended) override unused added_tokens via `added_tokens_overrides: `. See [config](../config.qmd) for more details.
+:::
+
+6. Continuing from the previous example, if you want to train on all EOT token trainable turns but only last EOS token, set `train_on_eos: last`.
+
+```yaml
+eot_tokens:
+  - "[/INST]"
+  # ...
+
+datasets:
+  - path: ...
+    type: chat_template
+
+    train_on_eos: last
+    train_on_eot: turn
+```
+
+::: {.callout-tip}
+If EOS token only appears at the end of a prompt, `train_on_eos: last` is equivalent to `train_on_eos: turn`. Therefore, generally, you can leave them to their defaults and omit them.
+:::
+
+
+7. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
 
 For a data sample that looks like:
 
@@ -162,3 +195,15 @@ datasets:
 ::: {.callout-tip}
 It is not necessary to set both `message_field_training` and `message_field_training_detail` at once.
 :::
+
+## sharegpt
+
+::: {.callout-important}
+ShareGPT is deprecated!. Please see [chat_template](#chat_template) section.
+:::
+
+## pygmalion
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"role": "...", "value": "..."}]}
+```

docs/faq.qmd

@@ -73,10 +73,40 @@ description: Frequently asked questions
 
 > A: This is likely an empty turn.
 
-**Q: The EOS/EOT token is incorrectly being masked or not being masked.**
+**Q: The EOS token is incorrectly being masked or not being masked / `EOS token __ not found in chat template`.**
 
-> A: This is because of the mismatch between `tokenizer.eos_token` and EOS/EOT token in template. Please make sure to set `eos_token` under `special_tokens` to the same EOS/EOT token as in template.
+> A: There can be two reasons:
+
+> 1. This is because of the mismatch between `tokenizer.eos_token` and EOS token in template. Please make sure to set `eos_token: ` under `special_tokens: ` to the same EOS token as in template.
+
+> 2. The EOS token is not in the template. Please check if your template is correct. As an example, `phi_35` template does not use its dedicated EOS token `<|endoftext|>` at the end.
 
 **Q: "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null. Please add a `chat_template` in tokenizer config"**
 
 > A: This is because the tokenizer does not have a chat template. Please add a chat template in the tokenizer config. See [chat_template](dataset-formats/conversation.qmd#chat-template) for more details.
+
+**Q: The EOT token(s) are incorrectly being masked or not being masked / `EOT token __ not found in chat template`.**
+
+> A: There can be two reasons:
+
+> 1. The EOT token is different from the EOS token and was not specified under `eot_tokens: `. Please set `eot_tokens: ` to the same EOT token(s) as in template.
+
+> 2. There is more than one EOT token per turn in the template. Please raise an issue with examples as we recognize this as an edge case.
+
+**Q: `EOT token encoding failed. Please check if the token is valid and can be encoded.`**
+
+> A: There could be some issue with the tokenizer or unicode encoding. Please raise an issue with examples with the EOT token & tokenizer causing the issue.
+
+**Q: `EOT token __ is encoded as multiple tokens.`**
+
+> A: This is because the EOT token is encoded as multiple tokens which can cause unexpected behavior. Please add it under `tokens: ` or (recommended) override unused added_tokens via `added_tokens_overrides: `.
+
+**Q: `Conflict between train_on_eos and train_on_eot. eos_token is in eot_tokens and train_on_eos != train_on_eot`**
+
+> A: This is because the EOS token is in the `eot_tokens: ` while mismatch between `train_on_eos: ` and `train_on_eot: `. This will cause one to override the other. Please ensure that `train_on_eos: ` and `train_on_eot: ` are the same or remove the EOS token from `eot_tokens: `.
+
+**Q: If `eot_tokens: ` is not provided, what happens?**
+
+> A: If `eot_tokens: ` is not provided, the default behavior is the same as before. EOS tokens used to delimit turns are masked/unmasked depending on whether the turn is trainable.
+
+> Internally, `eot_tokens: tokenizer.eos_token` and `train_on_eot: train_on_eos` (which defaults to `turn`). This transition helps clarify the naming and behavior of EOT/EOS tokens.

docs/multimodal.qmd

@@ -164,7 +164,7 @@ Here is an example of a multi-modal dataset:
         {
             "role": "user",
             "content": [
-                {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
+                {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
                 {"type": "text", "text": "Describe this image in detail."}
             ]
         },

docs/rlhf.qmd

@@ -502,9 +502,7 @@ The input format is a simple JSON input with customizable fields based on the ab
 Check out our [GRPO cookbook](https://github.com/axolotl-ai-cloud/axolotl-cookbook/tree/main/grpo#training-an-r1-style-large-language-model-using-grpo).
 :::
 
-If you have multiple GPUs available, we reccomend using `vLLM` with the `GRPOTrainer` to significantly speedup trajectory generation during training.
-First, launch a `vLLM` server using `trl vllm-serve` - you may use a config file or CLI overrides to configure your vLLM server. In this example, we're
-using 4 GPUs - 2 for training, and 2 for vLLM:
+In the latest GRPO implementation, `vLLM` is used to significantly speedup trajectory generation during training. In this example, we're using 4 GPUs - 2 for training, and 2 for vLLM:
 
 ::: {.callout-important}
 Make sure you've installed the correct version of vLLM by including it as an extra when installing axolotl, e.g. `pip install axolotl[vllm]`.
@@ -539,6 +537,10 @@ Your `vLLM` instance will now attempt to spin up, and it's time to kick off trai
 CUDA_VISIBLE_DEVICES=0,1 axolotl train grpo.yaml --num-processes 2
 ```
 
+::: {.callout-note}
+Due to TRL's implementation with vLLM, the vLLM instance must use the last N GPUs instead of the first N GPUs. This is why in the example above, we use `CUDA_VISIBLE_DEVICES=2,3` for the vLLM instance.
+:::
+
 #### Reward functions
 
 GRPO uses custom reward functions and transformations. Please have them ready locally.

examples/llama-3/sparse-finetuning.yaml

@@ -0,0 +1,77 @@
+base_model: neuralmagic/Sparse-Llama-3.1-8B-2of4
+
+plugins:
+  - axolotl.integrations.llm_compressor.LLMCompressorPlugin
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.05
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+eval_sample_packing: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: <|end_of_text|>
+
+llmcompressor:
+  recipe:
+    finetuning_stage:
+      finetuning_modifiers:
+        ConstantPruningModifier:
+          targets: [
+            're:.*q_proj.weight',
+            're:.*k_proj.weight',
+            're:.*v_proj.weight',
+            're:.*o_proj.weight',
+            're:.*gate_proj.weight',
+            're:.*up_proj.weight',
+            're:.*down_proj.weight',
+          ]
+          start: 0
+  save_compressed: true

examples/qwen3/32b-qlora.yaml

@@ -0,0 +1,69 @@
+base_model: Qwen/Qwen3-32B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+strict: false
+
+chat_template: qwen3
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+val_set_size: 0.0
+output_dir: ./outputs/out
+dataset_prepared_path: last_run_prepared
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+pad_to_sequence_len: true
+
+load_in_4bit: true
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - down_proj
+  - up_proj
+lora_mlp_kernel: true
+lora_qkv_kernel: true
+lora_o_kernel: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: offload
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/qwen3/qlora-fsdp.yaml

@@ -0,0 +1,68 @@
+base_model: Qwen/Qwen3-8B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./outputs/out
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+pad_to_sequence_len: true
+
+adapter: qlora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 64
+lora_dropout: 0.05
+lora_target_linear: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+special_tokens:

requirements.txt

@@ -11,14 +11,14 @@ liger-kernel==0.5.8
 
 packaging==23.2
 
-peft==0.15.1
+peft==0.15.2
 transformers==4.51.3
 tokenizers>=0.21.1
 accelerate==1.6.0
 datasets==3.5.0
 deepspeed>=0.15.4
-trl==0.16.1
-hf_xet==1.0.0
+trl==0.17.0
+hf_xet==1.1.0
 hqq==0.2.5
 
 optimum==1.16.2

setup.py

@@ -67,13 +67,13 @@ def parse_requirements(extras_require_map):
             if (major, minor) >= (2, 7):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 # _install_requires.append("xformers==0.0.29.post3")  # xformers seems to be hard pinned to 2.6.0
-                extras_require_map["vllm"] = ["vllm==0.8.3"]
+                extras_require_map["vllm"] = ["vllm==0.8.5"]
             elif (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append(
                     "xformers==0.0.29.post2"
                 )  # vllm needs post2 w torch 2.6
-                extras_require_map["vllm"] = ["vllm==0.8.3"]
+                extras_require_map["vllm"] = ["vllm==0.8.5"]
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:
@@ -149,6 +149,9 @@ extras_require = {
     "vllm": [
         "vllm==0.7.2",
     ],
+    "llmcompressor": [
+        "llmcompressor==0.5.1",
+    ],
 }
 
 install_requires, dependency_links, extras_require_build = parse_requirements(

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.8.0"
+__version__ = "0.10.0.dev0"

src/axolotl/cli/__init__.py

@@ -2,4 +2,7 @@
 
 import os
 
+from axolotl.logging_config import configure_logging
+
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+configure_logging()

src/axolotl/cli/art.py

@@ -16,8 +16,15 @@ AXOLOTL_LOGO = """
     @@@@  @@@@@@@@@@@@@@@@
 """
 
+HAS_PRINTED_LOGO = False
+
 
 def print_axolotl_text_art():
     """Prints axolotl ASCII art."""
+
+    global HAS_PRINTED_LOGO  # pylint: disable=global-statement
+    if HAS_PRINTED_LOGO:
+        return
     if is_main_process():
+        HAS_PRINTED_LOGO = True
         print(AXOLOTL_LOGO)

src/axolotl/cli/checks.py

@@ -8,9 +8,6 @@ from accelerate.commands.config import config_args
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
 
-from axolotl.logging_config import configure_logging
-
-configure_logging()
 LOG = logging.getLogger(__name__)
 
 

src/axolotl/cli/config.py

@@ -5,6 +5,7 @@ import logging
 import os
 import tempfile
 from pathlib import Path
+from tempfile import NamedTemporaryFile
 from typing import Union
 from urllib.parse import urlparse
 
@@ -152,7 +153,15 @@ def prepare_plugins(cfg: DictDefault):
             plugin_manager.register(plugin_name)
 
 
-def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefault:
+def plugin_set_cfg(cfg: DictDefault):
+    if cfg.get("plugins"):
+        plugin_manager = PluginManager.get_instance()
+        plugin_manager.cfg = cfg
+
+
+def load_cfg(
+    config: str | Path | DictDefault = Path("examples/"), **kwargs
+) -> DictDefault:
     """
     Loads the `axolotl` configuration stored at `config`, validates it, and performs
     various setup.
@@ -164,13 +173,24 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
     Returns:
         `DictDefault` mapping configuration keys to values.
     """
-    config = check_remote_config(config)
-    if Path(config).is_dir():
-        config = choose_config(Path(config))
+    if isinstance(config, (str, Path)):
+        config = check_remote_config(config)
+        if Path(config).is_dir():
+            config = choose_config(Path(config))
 
-    # Load the config from the yaml file
-    with open(config, encoding="utf-8") as file:
-        cfg: DictDefault = DictDefault(yaml.safe_load(file))
+        # Load the config from the yaml file
+        with open(config, encoding="utf-8") as file:
+            cfg: DictDefault = DictDefault(yaml.safe_load(file))
+
+        cfg.axolotl_config_path = config
+    else:
+        cfg = config
+        with NamedTemporaryFile(
+            mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
+        ) as temp_file:
+            temp_file.write(yaml.dump(config.to_dict()))
+            temp_file.close()
+        cfg.axolotl_config_path = temp_file.name
 
     # If there are any options passed in the cli, if it is something that seems valid
     # from the yaml, then overwrite the value
@@ -184,8 +204,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
             else:
                 cfg[k] = kwargs[k]
 
-    cfg.axolotl_config_path = config
-
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)
@@ -213,5 +231,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
     setup_wandb_env_vars(cfg)
     setup_mlflow_env_vars(cfg)
     setup_comet_env_vars(cfg)
+    plugin_set_cfg(cfg)
 
     return cfg

src/axolotl/cli/evaluate.py

@@ -1,6 +1,7 @@
 """CLI to run evaluation on a model."""
 
 import logging
+import os
 from pathlib import Path
 from typing import Union
 
@@ -14,6 +15,7 @@ from axolotl.cli.checks import check_accelerate_default_config, check_user_token
 from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.evaluate import evaluate
+from axolotl.utils import patch_optimized_env
 from axolotl.utils.dict import DictDefault
 
 LOG = logging.getLogger(__name__)
@@ -29,10 +31,14 @@ def do_evaluate(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: CLI arguments.
     """
+    # Enable expandable segments for cuda allocation to improve VRAM usage
+    patch_optimized_env()
+
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
     check_accelerate_default_config()
-    check_user_token()
+    if int(os.getenv("LOCAL_RANK", "0")) == 0:
+        check_user_token()
 
     if cfg.rl:
         dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)

src/axolotl/cli/main.py

@@ -28,9 +28,8 @@ from axolotl.cli.utils import (
     fetch_from_github,
     filter_none_kwargs,
 )
-from axolotl.cli.vllm_serve import do_vllm_serve
 from axolotl.integrations.lm_eval.cli import lm_eval
-from axolotl.utils import set_pytorch_cuda_alloc_conf
+from axolotl.utils import patch_optimized_env
 from axolotl.utils.schemas.config import AxolotlInputConfig
 
 
@@ -56,6 +55,8 @@ def preprocess(config: str, cloud: Optional[str] = None, **kwargs) -> None:
         kwargs: Additional keyword arguments which correspond to CLI args or `axolotl`
             config options.
     """
+    patch_optimized_env()
+
     if cloud:
         from axolotl.cli.cloud import do_cli_preprocess
 
@@ -101,7 +102,7 @@ def train(
             config options.
     """
     # Enable expandable segments for cuda allocation to improve VRAM usage
-    set_pytorch_cuda_alloc_conf()
+    patch_optimized_env()
 
     if "use_ray" in kwargs and kwargs["use_ray"]:
         accelerate = False
@@ -327,6 +328,8 @@ def fetch(directory: str, dest: Optional[str]) -> None:
 @add_options_from_dataclass(VllmServeCliArgs)
 @filter_none_kwargs
 def vllm_serve(config: str, **cli_args: VllmServeCliArgs):
+    from axolotl.cli.vllm_serve import do_vllm_serve
+
     do_vllm_serve(config, cli_args)
 
 

src/axolotl/cli/train.py

@@ -1,5 +1,6 @@
 """CLI to run training on a model."""
 
+import gc
 import logging
 import os
 from pathlib import Path
@@ -17,7 +18,7 @@ from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
 from axolotl.train import train
-from axolotl.utils import set_pytorch_cuda_alloc_conf
+from axolotl.utils import patch_optimized_env
 from axolotl.utils.config import normalize_config, resolve_dtype
 from axolotl.utils.dict import DictDefault
 
@@ -35,7 +36,7 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
         cli_args: Training-specific CLI arguments.
     """
     # Enable expandable segments for cuda allocation to improve VRAM usage
-    set_pytorch_cuda_alloc_conf()
+    patch_optimized_env()
 
     print_axolotl_text_art()
     check_accelerate_default_config()
@@ -48,8 +49,11 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
     model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
+
     del model, tokenizer, trainer
 
+    gc.collect()
+
     plugin_manager = PluginManager.get_instance()
     plugin_manager.post_train_unload(cfg)
 

src/axolotl/cli/utils.py

@@ -20,11 +20,9 @@ from transformers import (
     ProcessorMixin,
 )
 
-from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model, load_processor, load_tokenizer
 
-configure_logging()
 LOG = logging.getLogger(__name__)
 
 

src/axolotl/common/architectures.py

@@ -11,5 +11,6 @@ MOE_ARCH_BLOCK = {
     ],
     "mixtral": "MixtralSparseMoeBlock",
     "qwen2_moe": "Qwen2MoeSparseMoeBlock",
+    "qwen3_moe": "Qwen3MoeSparseMoeBlock",
     "deepseek_v2": "DeepseekV2MoE",
 }

src/axolotl/common/datasets.py

@@ -47,7 +47,8 @@ def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:
 def load_datasets(
     *,
     cfg: DictDefault,
-    cli_args: Union[PreprocessCliArgs, TrainerCliArgs],
+    cli_args: PreprocessCliArgs | TrainerCliArgs | None = None,
+    debug: bool = False,
 ) -> TrainDatasetMeta:
     """
     Loads one or more training or evaluation datasets, calling
@@ -56,6 +57,7 @@ def load_datasets(
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Command-specific CLI arguments.
+        debug: Whether to print out tokenization of sample
 
     Returns:
         Dataclass with fields for training and evaluation datasets and the computed
@@ -64,7 +66,8 @@ def load_datasets(
     tokenizer = load_tokenizer(cfg)
     processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
     preprocess_iterable = (
-        hasattr(cli_args, "iterable")
+        cli_args
+        and hasattr(cli_args, "iterable")
         and cli_args.iterable is not None
         and cli_args.iterable
     )
@@ -76,20 +79,25 @@ def load_datasets(
         preprocess_iterable=preprocess_iterable,
     )
 
-    if (
-        cli_args.debug
-        or cfg.debug
-        or cli_args.debug_text_only
-        or int(cli_args.debug_num_examples) > 0
-    ):
+    if (  # pylint: disable=too-many-boolean-expressions
+        cli_args
+        and (
+            cli_args.debug
+            or cfg.debug
+            or cli_args.debug_text_only
+            or int(cli_args.debug_num_examples) > 0
+        )
+    ) or debug:
         LOG.info("check_dataset_labels...")
 
-        train_samples = sample_dataset(train_dataset, cli_args.debug_num_examples)
+        num_examples = cli_args.debug_num_examples if cli_args else 1
+        text_only = cli_args.debug_text_only if cli_args else False
+        train_samples = sample_dataset(train_dataset, num_examples)
         check_dataset_labels(
             train_samples,
             tokenizer,
-            num_examples=cli_args.debug_num_examples,
-            text_only=cli_args.debug_text_only,
+            num_examples=num_examples,
+            text_only=text_only,
         )
 
         LOG.info("printing prompters...")

src/axolotl/core/trainer_builder.py

@@ -60,6 +60,7 @@ from axolotl.core.training_args import (
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback
+from axolotl.monkeypatch.trainer.lr import patch_trainer_get_lr
 from axolotl.processing_strategies import get_processing_strategy
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
@@ -114,6 +115,8 @@ class TrainerBuilderBase(abc.ABC):
         if hasattr(model, "add_model_tags"):
             model.add_model_tags(["axolotl"])
 
+        patch_trainer_get_lr()
+
     @property
     def model_ref(self):
         return self._model_ref
@@ -485,7 +488,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         # these are all the "standard" kwargs that are def used
         training_arguments_kwargs["max_steps"] = (
-            total_num_steps if self.cfg.max_steps else -1
+            self.cfg.max_steps if self.cfg.max_steps else -1
         )
         training_arguments_kwargs["max_seq_length"] = self.cfg.sequence_len
         training_arguments_kwargs["per_device_train_batch_size"] = (

src/axolotl/core/trainers/base.py

@@ -114,6 +114,8 @@ class AxolotlTrainer(
             packing_efficiency_estimate=self.args.sample_packing_efficiency,
             batch_max_len=batch_max_len,
             batch_size=batch_size,
+            group_size=self.args.sample_packing_group_size,
+            bin_size=self.args.sample_packing_bin_size,
             sequential=self.args.sample_packing_sequentially,
             drop_last=True,
         )

src/axolotl/core/trainers/dpo/trainer.py

@@ -3,15 +3,29 @@ DPO trainer for axolotl
 """
 
 import gc
+import random
 from functools import wraps
-from typing import Any, Dict, Union
+from typing import Any, Dict, Optional, Union
 
+import pandas as pd
 import torch
+import wandb
+from accelerate import PartialState
+from datasets import Dataset, IterableDataset
 from peft.optimizers import create_loraplus_optimizer
 from torch import nn
-from transformers import Trainer
+from torch.utils.data import DataLoader
+from transformers import (
+    BaseImageProcessor,
+    FeatureExtractionMixin,
+    PreTrainedTokenizerBase,
+    ProcessorMixin,
+    Trainer,
+)
+from transformers.trainer_utils import EvalLoopOutput
 from transformers.utils import is_sagemaker_mp_enabled
-from trl import DPOTrainer
+from trl import DPOConfig, DPOTrainer, maybe_apply_chat_template, maybe_extract_prompt
+from trl.trainer.utils import log_table_to_comet_experiment
 
 from axolotl.core.trainers.mixins import RngLoaderMixin, SchedulerMixin
 from axolotl.core.trainers.utils import (
@@ -81,6 +95,64 @@ class AxolotlDPOTrainer(RngLoaderMixin, SchedulerMixin, DPOTrainer):
 
         return super().push_to_hub(*args, **kwargs)
 
+    # TODO: remove this once https://github.com/huggingface/trl/pull/3377 is in a release
+    def _prepare_dataset(
+        self,
+        dataset: Union[Dataset, IterableDataset],
+        processing_class: Union[
+            PreTrainedTokenizerBase,
+            BaseImageProcessor,
+            FeatureExtractionMixin,
+            ProcessorMixin,
+        ],
+        args: DPOConfig,
+        dataset_name: str,
+    ) -> Union[Dataset, IterableDataset]:
+        # Build the kwargs for the `map` function
+        map_kwargs: Dict[str, Any] = {"writer_batch_size": 10}
+        if isinstance(dataset, Dataset):  # IterableDataset does not support num_proc
+            map_kwargs["num_proc"] = args.dataset_num_proc
+
+        with PartialState().main_process_first():
+            # Extract prompt if needed
+            if isinstance(
+                dataset, Dataset
+            ):  # `IterableDataset.map` does not support `desc`
+                map_kwargs["desc"] = f"Extracting prompt in {dataset_name} dataset"
+            dataset = dataset.map(maybe_extract_prompt, **map_kwargs)
+
+            # Apply the chat template if needed
+            if isinstance(
+                dataset, Dataset
+            ):  # `IterableDataset.map` does not support `desc`
+                map_kwargs["desc"] = f"Applying chat template to {dataset_name} dataset"
+            dataset = dataset.map(
+                maybe_apply_chat_template,
+                fn_kwargs={"tokenizer": processing_class, "tools": args.tools},
+                **map_kwargs,
+            )
+
+            # Tokenize the dataset
+            if isinstance(
+                dataset, Dataset
+            ):  # `IterableDataset.map` does not support `desc`
+                map_kwargs["desc"] = f"Tokenizing {dataset_name} dataset"
+
+            dataset = dataset.map(
+                self.tokenize_row if not self.is_vision_model else self.process_row,
+                remove_columns=["chosen", "rejected"],
+                fn_kwargs={
+                    "processing_class": processing_class,
+                    "max_prompt_length": args.max_prompt_length,
+                    "max_completion_length": args.max_completion_length,
+                    # for enc-dec, we add the special tokens ([bos_token] + prompt + [eos_token]; completion + [eos_token])
+                    "add_special_tokens": False,
+                },
+                **map_kwargs,
+            )
+
+        return dataset
+
     @staticmethod
     def tokenize_row(
         features,
@@ -105,12 +177,8 @@ class AxolotlDPOTrainer(RngLoaderMixin, SchedulerMixin, DPOTrainer):
             # dpo trainer may incorrectly prepend the bos_token_id to the dpo outputs
             if res["chosen_input_ids"][0] == processing_class.bos_token_id:
                 res["chosen_input_ids"] = res["chosen_input_ids"][1:]
-                res["chosen_labels"] = res["chosen_labels"][1:]
-                res["chosen_attention_mask"] = res["chosen_attention_mask"][1:]
             if res["rejected_input_ids"][0] == processing_class.bos_token_id:
                 res["rejected_input_ids"] = res["rejected_input_ids"][1:]
-                res["rejected_labels"] = res["rejected_labels"][1:]
-                res["rejected_attention_mask"] = res["rejected_attention_mask"][1:]
 
         return res
 
@@ -124,3 +192,67 @@ class AxolotlDPOTrainer(RngLoaderMixin, SchedulerMixin, DPOTrainer):
         gc.collect()
         torch.cuda.empty_cache()
         return loss
+
+    # TODO: remove this once https://github.com/huggingface/trl/pull/3377 is in a release
+    def evaluation_loop(
+        self,
+        dataloader: DataLoader,
+        description: str,
+        prediction_loss_only: Optional[bool] = None,
+        ignore_keys: Optional[list[str]] = None,
+        metric_key_prefix: str = "eval",
+    ) -> EvalLoopOutput:
+        """
+        Overriding built-in evaluation loop to store metrics for each batch.
+        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.
+
+        Works both with or without labels.
+        """
+
+        # Sample and save to game log if requested (for one batch to save time)
+        if self.generate_during_eval:
+            # Generate random indices within the range of the total number of samples
+            num_samples = len(dataloader.dataset)
+            random_indices = random.sample(
+                range(num_samples), k=self.args.eval_batch_size
+            )
+
+            # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader
+            random_batch_dataset = dataloader.dataset.select(random_indices)
+            random_batch = self.data_collator(random_batch_dataset)
+            random_batch = self._prepare_inputs(random_batch)
+
+            policy_output_decoded, ref_output_decoded = (
+                self.generate_from_model_and_ref(self.model, random_batch)
+            )
+
+            table = pd.DataFrame(
+                columns=["Prompt", "Policy", "Ref Model"],
+                data=[
+                    [prompt, pol[len(prompt) :], ref[len(prompt) :]]
+                    for prompt, pol, ref in zip(
+                        random_batch_dataset["prompt"],
+                        policy_output_decoded,
+                        ref_output_decoded,
+                    )
+                ],
+            )
+            if "wandb" in self.args.report_to and self.accelerator.is_main_process:
+                wandb.log({"game_log": wandb.Table(data=table)})
+
+            if "comet_ml" in self.args.report_to:
+                log_table_to_comet_experiment(
+                    name="game_log.csv",
+                    table=table,
+                )
+
+        # Base evaluation
+        initial_output = super().evaluation_loop(
+            dataloader,
+            description,
+            prediction_loss_only,
+            ignore_keys,
+            metric_key_prefix,
+        )
+
+        return initial_output

src/axolotl/core/trainers/grpo/__init__.py

@@ -63,6 +63,7 @@ class GRPOStrategy:
 
         grpo_args_kwargs["max_completion_length"] = trl.max_completion_length
         grpo_args_kwargs["log_completions"] = trl.log_completions
+        grpo_args_kwargs["num_completions_to_print"] = trl.num_completions_to_print
 
         if trl.reward_weights:
             grpo_args_kwargs["reward_weights"] = trl.reward_weights
@@ -70,6 +71,13 @@ class GRPOStrategy:
         if trl.scale_rewards is not None:
             grpo_args_kwargs["scale_rewards"] = trl.scale_rewards
 
+        if trl.loss_type is not None:
+            grpo_args_kwargs["loss_type"] = trl.loss_type
+        if trl.mask_truncated_completions is not None:
+            grpo_args_kwargs["mask_truncated_completions"] = (
+                trl.mask_truncated_completions
+            )
+
         if trl.temperature is not None:
             grpo_args_kwargs["temperature"] = trl.temperature
         if trl.top_p is not None:
@@ -85,6 +93,11 @@ class GRPOStrategy:
             grpo_args_kwargs["num_iterations"] = trl.num_iterations
         if trl.epsilon is not None:
             grpo_args_kwargs["epsilon"] = trl.epsilon
+        if trl.epsilon_high is not None:
+            grpo_args_kwargs["epsilon_high"] = trl.epsilon_high
+
+        if trl.use_liger_loss is not None:
+            grpo_args_kwargs["use_liger_loss"] = trl.use_liger_loss
 
         return grpo_args_kwargs
 
@@ -135,7 +148,9 @@ class GRPOStrategy:
         try:
             # use importlib to dynamically load the reward function from the module
             reward_func_module_name = reward_func_fqn.split(".")[-1]
-            reward_func_module = importlib.import_module(reward_func_fqn.split(".")[-2])
+            reward_func_module = importlib.import_module(
+                ".".join(reward_func_fqn.split(".")[:-1])
+            )
             reward_func = getattr(reward_func_module, reward_func_module_name)
             if not len(inspect.signature(reward_func).parameters) >= 2:
                 raise ValueError(

src/axolotl/core/trainers/mixins/scheduler.py

@@ -3,9 +3,10 @@
 import logging
 
 import torch
-from torch.optim.lr_scheduler import OneCycleLR
+from torch.optim.lr_scheduler import LRScheduler, OneCycleLR
 from transformers.trainer import Trainer
 
+from axolotl.integrations.base import PluginManager
 from axolotl.utils.schedulers import (
     RexLR,
     get_cosine_schedule_with_min_lr,
@@ -25,9 +26,9 @@ class SchedulerMixin(Trainer):
 
     def create_scheduler(
         self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
-    ):
+    ) -> LRScheduler:
         """
-        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
+        Set up the scheduler. The optimizer of the trainer must have been set up either before this method is called or
         passed as an argument.
 
         Args:
@@ -47,7 +48,16 @@ class SchedulerMixin(Trainer):
         # fmt: off
         if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
             # fmt: on
-            if self.args.alternate_lr_scheduler_type == "one_cycle":
+            plugin_manager = PluginManager.get_instance()
+            lr_scheduler: LRScheduler | None = plugin_manager.create_lr_scheduler(
+                trainer=self,
+                optimizer=optimizer,
+                num_training_steps=num_training_steps
+            )
+            if lr_scheduler is not None:
+                LOG.info(f"Using plugin-created lr_scheduler: {lr_scheduler}")
+                self.lr_scheduler = lr_scheduler
+            elif self.args.alternate_lr_scheduler_type == "one_cycle":
                 num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
                 pct_start = num_warmup_steps / num_training_steps
                 extra_lr_kwargs = {}
@@ -110,4 +120,4 @@ class SchedulerMixin(Trainer):
             if use_cosine_min_lr:
                 LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
 
-        return self.lr_scheduler
+        return self.lr_scheduler  # type: ignore

src/axolotl/core/trainers/relora.py

@@ -1,6 +1,7 @@
 """Module for ReLoRA trainer"""
 
 import torch
+from torch.optim.lr_scheduler import LRScheduler
 
 from axolotl.core.trainers.base import AxolotlTrainer
 from axolotl.monkeypatch.relora import ReLoRAScheduler
@@ -19,9 +20,11 @@ class ReLoRATrainer(AxolotlTrainer):
         self,
         num_training_steps: int,
         optimizer: torch.optim.Optimizer | None = None,
-    ):
+    ) -> LRScheduler:
         optimizer = self.optimizer if optimizer is None else optimizer
-        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
+        lr_scheduler: LRScheduler = super().create_scheduler(
+            num_training_steps, optimizer
+        )
 
         if self.args.relora_steps:
             warmup_steps = (
@@ -30,7 +33,7 @@ class ReLoRATrainer(AxolotlTrainer):
             anneal_steps = (
                 self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
             )
-            self.lr_scheduler = ReLoRAScheduler(
+            self.lr_scheduler = ReLoRAScheduler(  # type: ignore
                 optimizer,
                 lr_scheduler,
                 self.args.relora_steps,
@@ -38,6 +41,6 @@ class ReLoRATrainer(AxolotlTrainer):
                 warmup_steps,
             )
         else:
-            self.lr_scheduler = lr_scheduler
+            self.lr_scheduler = lr_scheduler  # type: ignore
 
-        return self.lr_scheduler
+        return self.lr_scheduler  # type: ignore

src/axolotl/evaluate.py

@@ -11,20 +11,19 @@ from accelerate.logging import get_logger
 from datasets import Dataset
 from transformers.trainer import Trainer
 
-from axolotl.logging_config import configure_logging
-from axolotl.train import TrainDatasetMeta
-from axolotl.utils import set_pytorch_cuda_alloc_conf
+from axolotl.train import (
+    TrainDatasetMeta,
+    setup_model_and_tokenizer,
+)
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
-from axolotl.utils.models import load_model, load_processor, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 src_dir = os.path.join(project_root, "src")
 sys.path.insert(0, src_dir)
 
-configure_logging()
-LOG = get_logger("axolotl.evaluate")
+LOG = get_logger(__name__)
 
 
 def evaluate_dataset(
@@ -75,37 +74,22 @@ def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, f
     Returns:
         Dictionary mapping metric names to their values.
     """
-    # pylint: disable=duplicate-code
-    # Enable expandable segments for cuda allocation to improve VRAM usage
-    set_pytorch_cuda_alloc_conf()
-
-    # Load tokenizer
-    LOG.debug(
-        f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
-        main_process_only=True,
-    )
-    tokenizer = load_tokenizer(cfg)
-
-    # Load processor for multimodal models if needed
-    processor = None
-    if cfg.is_multimodal:
-        processor = load_processor(cfg, tokenizer)
+    # Load tokenizer, processor and model
+    LOG.debug("loading model for evaluation...")
+    model, tokenizer, _, processor = setup_model_and_tokenizer(cfg)
 
     # Get datasets
+    # pylint: disable=duplicate-code
     train_dataset = dataset_meta.train_dataset
     eval_dataset = dataset_meta.eval_dataset
     total_num_steps = dataset_meta.total_num_steps
 
-    # Load model
-    LOG.debug("loading model for evaluation...")
-    model, _ = load_model(cfg, tokenizer, processor=processor)
-
     # Set up trainer
     trainer = setup_trainer(
-        cfg,
+        cfg=cfg,
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
-        model=(model, None, None),  # No need for model_ref or peft_config
+        model=model,
         tokenizer=tokenizer,
         processor=processor,
         total_num_steps=total_num_steps,

src/axolotl/integrations/base.py

@@ -24,6 +24,7 @@ import logging
 from typing import OrderedDict
 
 import torch
+from torch.optim.lr_scheduler import LRScheduler
 
 
 class BasePlugin:
@@ -36,11 +37,12 @@ class BasePlugin:
     Methods:
     register(cfg): Registers the plugin with the given configuration.
     pre_model_load(cfg): Performs actions before the model is loaded.
-    post_model_load(cfg, model): Performs actions after the model is loaded.
+    post_model_build(cfg, model): Performs actions after the model is loaded, but before LoRA adapters are applied.
     pre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.
     post_lora_load(cfg, model): Performs actions after LoRA weights are loaded.
+    post_model_load(cfg, model): Performs actions after the model is loaded, inclusive of any adapters.
     create_optimizer(cfg, trainer): Creates and returns an optimizer for training.
-    create_lr_scheduler(cfg, trainer, optimizer): Creates and returns a learning rate scheduler.
+    create_lr_scheduler(cfg, trainer, optimizer, num_training_steps): Creates and returns a learning rate scheduler.
     add_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.
     add_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.
     """
@@ -77,6 +79,14 @@ class BasePlugin:
         None
         """
 
+    def post_model_build(self, cfg, model):  # pylint: disable=unused-argument
+        """
+        Performs actions after the model is built/loaded, but before any adapters are applied.
+
+        Args:
+            cfg (dict): The configuration for the plugin.
+        """
+
     def post_model_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after the model is loaded.
@@ -137,8 +147,8 @@ class BasePlugin:
         """
 
     def create_lr_scheduler(
-        self, cfg, trainer, optimizer
-    ):  # pylint: disable=unused-argument
+        self, cfg, trainer, optimizer, num_training_steps
+    ) -> LRScheduler | None:  # pylint: disable=unused-argument
         """
         Creates and returns a learning rate scheduler.
 
@@ -146,9 +156,10 @@ class BasePlugin:
         cfg (dict): The configuration for the plugin.
         trainer (object): The trainer object for training.
         optimizer (object): The optimizer for training.
+        num_training_steps (int): Total number of training steps
 
         Returns:
-        object: The created learning rate scheduler.
+        object (LRScheduler): The created learning rate scheduler.
         """
 
     def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
@@ -261,6 +272,7 @@ class PluginManager:
     plugins: OrderedDict[str, BasePlugin] = collections.OrderedDict()
 
     _instance = None
+    _cfg = None
 
     def __new__(cls):
         """
@@ -268,7 +280,9 @@ class PluginManager:
         """
         if cls._instance is None:
             cls._instance = super(PluginManager, cls).__new__(cls)
-            cls._instance.plugins = collections.OrderedDict()
+            cls._instance.plugins: OrderedDict[str, BasePlugin] = (
+                collections.OrderedDict()
+            )
         return cls._instance
 
     @staticmethod
@@ -281,6 +295,14 @@ class PluginManager:
             PluginManager()
         return PluginManager._instance  # type: ignore
 
+    @property
+    def cfg(self):
+        return self._cfg
+
+    @cfg.setter
+    def cfg(self, cfg):
+        self._cfg = cfg
+
     def register(self, plugin_name: str):
         """
         Registers a new plugin by its name.
@@ -329,9 +351,22 @@ class PluginManager:
         for plugin in self.plugins.values():
             plugin.pre_model_load(cfg)
 
+    def post_model_build(self, cfg, model):
+        """
+        Calls the post_model_build method of all registered plugins after the model has been built/loaded,
+        but before any adapters have been applied.
+
+        Args:
+            cfg (dict): The configuration for the plugins.
+            model (object): The loaded model.
+        """
+        for plugin in self.plugins.values():
+            plugin.post_model_build(cfg, model)
+
     def post_model_load(self, cfg, model):
         """
-        Calls the post_model_load method of all registered plugins.
+        Calls the post_model_load method of all registered plugins after the model has been loaded
+        inclusive of any adapters
 
         Parameters:
         cfg (dict): The configuration for the plugins.
@@ -387,29 +422,29 @@ class PluginManager:
                 return trainer_cls
         return None
 
-    def create_optimizer(self, cfg, trainer):
+    def create_optimizer(self, trainer):
         """
         Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer.
 
         Parameters:
-        cfg (dict): The configuration for the plugins.
         trainer (object): The trainer object for training.
 
         Returns:
         object: The created optimizer, or None if none was found.
         """
         for plugin in self.plugins.values():
-            optimizer = plugin.create_optimizer(cfg, trainer)
+            optimizer = plugin.create_optimizer(self.cfg, trainer)
             if optimizer is not None:
                 return optimizer
         return None
 
-    def create_lr_scheduler(self, cfg, trainer, optimizer):
+    def create_lr_scheduler(
+        self, trainer, optimizer, num_training_steps
+    ) -> LRScheduler | None:
         """
         Calls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.
 
         Parameters:
-        cfg (dict): The configuration for the plugins.
         trainer (object): The trainer object for training.
         optimizer (object): The optimizer for training.
 
@@ -417,7 +452,12 @@ class PluginManager:
         object: The created learning rate scheduler, or None if none was found.
         """
         for plugin in self.plugins.values():
-            scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
+            scheduler: LRScheduler | None = plugin.create_lr_scheduler(
+                self.cfg,
+                trainer=trainer,
+                optimizer=optimizer,
+                num_training_steps=num_training_steps,
+            )
             if scheduler is not None:
                 return scheduler
         return None
@@ -458,6 +498,20 @@ class PluginManager:
                 callbacks.extend(plugin_callbacks)
         return callbacks
 
+    def post_train(self, cfg, model):
+        """
+        Calls the post_train method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins.values():
+            plugin.post_train(cfg, model)
+
     def post_train_unload(self, cfg):
         """
         Calls the post_train_unload method of all registered plugins.

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -32,8 +32,8 @@ plugins:
 ## Supported Models
 
 - llama
-- llama4_text
 - llama4
+- llama4_text
 - mllama
 - phi3
 - gemma
@@ -43,6 +43,11 @@ plugins:
 - mistral
 - mistral3
 - qwen2
+- qwen2_moe
+- qwen2_vl
+- qwen2_5_vl
+- qwen3
+- qwen3_moe
 - cohere
 - cohere2
 - glm

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -25,7 +25,7 @@ import torch
 
 from axolotl.integrations.base import BasePlugin
 from axolotl.utils import get_pytorch_version
-from axolotl.utils.distributed import zero_only
+from axolotl.utils.distributed import is_main_process
 
 from .args import CutCrossEntropyArgs  # pylint: disable=unused-import. # noqa: F401
 
@@ -72,11 +72,11 @@ class CutCrossEntropyPlugin(BasePlugin):
         if cfg.cut_cross_entropy:
             self._check_requirements()
 
-            from axolotl.integrations.cut_cross_entropy.monkeypatch.patch import (
+            from .monkeypatch.patch import (
                 cce_patch,
             )
 
-            with zero_only():
+            if is_main_process(use_environ=True):
                 LOG.info(
                     f"Applying Cut Cross Entropy to model type: {cfg.model_config_type}"
                 )

src/axolotl/integrations/cut_cross_entropy/monkeypatch/llama.py

@@ -0,0 +1,174 @@
+"""Llama CCE patch. Adapted from transformers v4.51.2"""
+
+# pylint: disable=duplicate-code
+
+
+from types import MethodType
+from typing import Optional, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+    apply_lce,
+)
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.models.llama.modeling_llama import (
+    _CONFIG_FOR_DOC,
+    LLAMA_INPUTS_DOCSTRING,
+    KwargsForCausalLM,
+)
+from transformers.processing_utils import Unpack
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from transformers.utils.deprecation import deprecate_kwarg
+from transformers.utils.generic import can_return_tuple
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+@can_return_tuple
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def cce_forward(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Cache] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **kwargs: Unpack[KwargsForCausalLM],
+) -> CausalLMOutputWithPast:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+    >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
+    >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs: BaseModelOutputWithPast = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs.last_hidden_state
+    if hidden_states is None:
+        raise ValueError("hidden_states is None")
+
+    loss = None
+    logits = None
+
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = (
+        slice(-logits_to_keep, None)
+        if isinstance(logits_to_keep, int)
+        else logits_to_keep
+    )
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states[:, slice_indices, :],
+            self.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            **kwargs,
+        )
+    else:
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+def patch_llama(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    """Patch Llama for CCE."""
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.llama import modeling_llama
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_llama.LlamaForCausalLM
+        ), f"Expected a LlamaForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward, maybe_model)
+        return maybe_model
+
+    modeling_llama.LlamaForCausalLM.forward = cce_forward
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/patch.py

@@ -5,9 +5,7 @@
 import transformers
 from cut_cross_entropy.cce_utils import LinearCrossEntropyImpl
 from cut_cross_entropy.linear_cross_entropy import LCE_IMPL_DEFAULT
-from cut_cross_entropy.transformers.llama import patch_llama
 from cut_cross_entropy.transformers.phi3 import patch_phi3
-from cut_cross_entropy.transformers.qwen2 import patch_qwen2
 from cut_cross_entropy.transformers.utils import PatchOptions, TransformersModelT
 
 from axolotl.integrations.cut_cross_entropy.monkeypatch.cohere import (
@@ -24,6 +22,9 @@ from axolotl.integrations.cut_cross_entropy.monkeypatch.glm4 import (
     patch_glm,
     patch_glm4,
 )
+from axolotl.integrations.cut_cross_entropy.monkeypatch.llama import (
+    patch_llama,
+)
 from axolotl.integrations.cut_cross_entropy.monkeypatch.llama4 import (
     patch_llama4,
     patch_llama4_text,
@@ -33,6 +34,22 @@ from axolotl.integrations.cut_cross_entropy.monkeypatch.mistral3 import (
     patch_mistral3,
 )
 from axolotl.integrations.cut_cross_entropy.monkeypatch.mllama import patch_mllama
+from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen2 import (
+    patch_qwen2,
+)
+from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen2_5_vl import (
+    patch_qwen2_5_vl,
+)
+from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen2_moe import (
+    patch_qwen2_moe,
+)
+from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen2_vl import (
+    patch_qwen2_vl,
+)
+from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen3 import patch_qwen3
+from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen3_moe import (
+    patch_qwen3_moe,
+)
 
 CUT_CROSS_ENTROPY_MODEL_MAPPING = {
     "llama": patch_llama,
@@ -47,6 +64,11 @@ CUT_CROSS_ENTROPY_MODEL_MAPPING = {
     "mistral": patch_mistral,
     "mistral3": patch_mistral3,
     "qwen2": patch_qwen2,
+    "qwen2_moe": patch_qwen2_moe,
+    "qwen2_vl": patch_qwen2_vl,
+    "qwen2_5_vl": patch_qwen2_5_vl,
+    "qwen3": patch_qwen3,
+    "qwen3_moe": patch_qwen3_moe,
     "cohere": patch_cohere,
     "cohere2": patch_cohere2,
     "glm": patch_glm,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2.py

@@ -0,0 +1,37 @@
+"""Qwen2 CCE patch. The model inherits Llama's modeling code and uses the same forward method."""
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+)
+
+
+def patch_qwen2(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    from transformers.models.qwen2 import modeling_qwen2
+
+    # Set the _PATCH_OPTS in the llama patch file
+    import axolotl.integrations.cut_cross_entropy.monkeypatch.llama as llama_patch
+
+    llama_patch._PATCH_OPTS = patch_options  # pylint: disable=protected-access
+
+    from axolotl.integrations.cut_cross_entropy.monkeypatch.llama import (
+        cce_forward,
+    )
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_qwen2.Qwen2ForCausalLM
+        ), f"Expected a Qwen2ForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward, maybe_model)
+        return maybe_model
+
+    modeling_qwen2.Qwen2ForCausalLM.forward = cce_forward
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2_5_vl.py

@@ -0,0 +1,246 @@
+"""Qwen2.5 VL CCE patch. Adapted from transformers v4.51.2"""
+
+# pylint: disable=duplicate-code
+
+
+from types import MethodType
+from typing import Optional, Tuple, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+    apply_lce,
+)
+from torch.nn import CrossEntropyLoss
+from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
+    Qwen2_5_VLCausalLMOutputWithPast,
+)
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+def cce_forward_multimodal(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[list[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    pixel_values: Optional[torch.Tensor] = None,
+    pixel_values_videos: Optional[torch.FloatTensor] = None,
+    image_grid_thw: Optional[torch.LongTensor] = None,
+    video_grid_thw: Optional[torch.LongTensor] = None,
+    rope_deltas: Optional[torch.LongTensor] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    second_per_grid_ts: Optional[torch.Tensor] = None,
+) -> Union[Tuple, Qwen2_5_VLCausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+
+    >>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+    >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+
+    >>> messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image"},
+                {"type": "text", "text": "What is shown in this image?"},
+            ],
+        },
+    ]
+    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
+    ```"""
+
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    if inputs_embeds is None:
+        inputs_embeds = self.model.embed_tokens(input_ids)
+        if pixel_values is not None:
+            pixel_values = pixel_values.type(self.visual.dtype)
+            image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+            n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
+            n_image_features = image_embeds.shape[0]
+            if n_image_tokens != n_image_features:
+                raise ValueError(
+                    f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+                )
+
+            mask = input_ids == self.config.image_token_id
+            mask_unsqueezed = mask.unsqueeze(-1)
+            mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
+            image_mask = mask_expanded.to(inputs_embeds.device)
+
+            image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)  # type: ignore
+
+        if pixel_values_videos is not None:
+            pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
+            video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
+            n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
+            n_video_features = video_embeds.shape[0]
+            if n_video_tokens != n_video_features:
+                raise ValueError(
+                    f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
+                )
+
+            mask = input_ids == self.config.video_token_id
+            mask_unsqueezed = mask.unsqueeze(-1)
+            mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
+            video_mask = mask_expanded.to(inputs_embeds.device)
+
+            video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+            inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)  # type: ignore
+
+        if attention_mask is not None:
+            attention_mask = attention_mask.to(inputs_embeds.device)
+
+    # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme
+    if position_ids is None and (attention_mask is None or attention_mask.ndim == 2):
+        # calculate RoPE index once per generation in the pre-fill stage only
+        if (
+            (cache_position is not None and cache_position[0] == 0)
+            or self.rope_deltas is None
+            or (past_key_values is None or past_key_values.get_seq_length() == 0)  # type: ignore
+        ):
+            position_ids, rope_deltas = self.get_rope_index(
+                input_ids,
+                image_grid_thw,
+                video_grid_thw,
+                second_per_grid_ts,
+                attention_mask,
+            )
+            self.rope_deltas = rope_deltas
+        # then use the prev pre-calculated rope-deltas to get the correct position ids
+        else:
+            batch_size, seq_length, _ = inputs_embeds.shape
+            delta = (
+                (cache_position[0] + self.rope_deltas).to(inputs_embeds.device)
+                if cache_position is not None
+                else 0
+            )
+            position_ids = torch.arange(seq_length, device=inputs_embeds.device)  # type: ignore
+            position_ids = position_ids.view(1, -1).expand(batch_size, -1)  # type: ignore
+            if cache_position is not None:  # otherwise `deltas` is an int `0`
+                delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)  # type: ignore
+            position_ids = position_ids.add(delta)  # type: ignore
+            position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)  # type: ignore
+
+    outputs = self.model(
+        input_ids=None,
+        position_ids=position_ids,
+        attention_mask=attention_mask,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+    )
+
+    hidden_states = outputs[0]
+    logits = None
+    loss = None
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states,
+            self.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+        )
+    else:
+        logits = self.lm_head(hidden_states)
+
+        if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return Qwen2_5_VLCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        rope_deltas=self.rope_deltas,
+    )
+
+
+def patch_qwen2_5_vl(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+
+    from transformers.models.qwen2_5_vl import modeling_qwen2_5_vl
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration
+        ), f"Expected a Qwen2_5_VLForConditionalGeneration model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
+
+        return maybe_model
+
+    modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration.forward = (
+        cce_forward_multimodal
+    )
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2_moe.py

@@ -0,0 +1,188 @@
+"""Qwen2 MoE CCE patch. Adapted from transformers v4.51.2"""
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+from typing import Optional, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+    apply_lce,
+)
+from transformers.models.qwen2_moe.modeling_qwen2_moe import (
+    _CONFIG_FOR_DOC,
+    QWEN2MOE_INPUTS_DOCSTRING,
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+    load_balancing_loss_func,
+)
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from transformers.utils.deprecation import deprecate_kwarg
+from transformers.utils.generic import can_return_tuple
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+@can_return_tuple
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def forward(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[list[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    output_router_logits: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **loss_kwargs,
+) -> MoeCausalLMOutputWithPast:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Qwen2MoeForCausalLM
+
+    >>> model = Qwen2MoeForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+    >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_router_logits = (
+        output_router_logits
+        if output_router_logits is not None
+        else self.config.output_router_logits
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs: MoeModelOutputWithPast = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        output_router_logits=output_router_logits,
+        cache_position=cache_position,
+    )
+
+    hidden_states = outputs.last_hidden_state
+    loss = None
+    logits = None
+
+    if hidden_states is None:
+        raise ValueError("hidden_states is None")
+
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = (
+        slice(-logits_to_keep, None)
+        if isinstance(logits_to_keep, int)
+        else logits_to_keep
+    )
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states[:, slice_indices, :],
+            self.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            **loss_kwargs,
+        )
+    else:
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits,
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        if labels is not None:
+            loss += self.router_aux_loss_coef * aux_loss.to(  # type: ignore
+                loss.device  # type: ignore
+            )  # make sure to reside in the same device
+
+    return MoeCausalLMOutputWithPast(
+        loss=loss,
+        aux_loss=aux_loss,  # type: ignore
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        router_logits=outputs.router_logits,
+    )
+
+
+def patch_qwen2_moe(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+
+    from transformers.models.qwen2_moe import modeling_qwen2_moe
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_qwen2_moe.Qwen2MoeForCausalLM
+        ), f"Expected a Qwen3MoeForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(forward, maybe_model)
+
+        return maybe_model
+
+    modeling_qwen2_moe.Qwen2MoeForCausalLM.forward = forward
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2_vl.py

@@ -0,0 +1,249 @@
+"""Qwen2 VL CCE patch. Adapted from transformers v4.51.2"""
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+from typing import Optional, Tuple, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+    apply_lce,
+)
+from torch.nn import CrossEntropyLoss
+from transformers.models.qwen2_vl.modeling_qwen2_vl import (
+    _CONFIG_FOR_DOC,
+    QWEN2_VL_INPUTS_DOCSTRING,
+    Qwen2VLCausalLMOutputWithPast,
+)
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+@add_start_docstrings_to_model_forward(QWEN2_VL_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=Qwen2VLCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def cce_forward_multimodal(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[list[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    pixel_values: Optional[torch.Tensor] = None,
+    pixel_values_videos: Optional[torch.FloatTensor] = None,
+    image_grid_thw: Optional[torch.LongTensor] = None,
+    video_grid_thw: Optional[torch.LongTensor] = None,
+    rope_deltas: Optional[torch.LongTensor] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+) -> Union[Tuple, Qwen2VLCausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+
+    >>> model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
+    >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
+
+    >>> messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image"},
+                {"type": "text", "text": "What is shown in this image?"},
+            ],
+        },
+    ]
+    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
+    ```"""
+
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    if inputs_embeds is None:
+        inputs_embeds = self.model.embed_tokens(input_ids)
+        if pixel_values is not None:
+            pixel_values = pixel_values.type(self.visual.get_dtype())
+            image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+            n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
+            n_image_features = image_embeds.shape[0]
+            if n_image_tokens != n_image_features:
+                raise ValueError(
+                    f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+                )
+            image_mask = (
+                (input_ids == self.config.image_token_id)
+                .unsqueeze(-1)
+                .expand_as(inputs_embeds)
+                .to(inputs_embeds.device)
+            )
+            image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)  # type: ignore
+
+        if pixel_values_videos is not None:
+            pixel_values_videos = pixel_values_videos.type(self.visual.get_dtype())
+            video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
+            n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
+            n_video_features = video_embeds.shape[0]
+            if n_video_tokens != n_video_features:
+                raise ValueError(
+                    f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
+                )
+            video_mask = (
+                (input_ids == self.config.video_token_id)
+                .unsqueeze(-1)
+                .expand_as(inputs_embeds)
+                .to(inputs_embeds.device)
+            )
+            video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+            inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)  # type: ignore
+
+        if attention_mask is not None:
+            attention_mask = attention_mask.to(inputs_embeds.device)
+
+    # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme
+    if position_ids is None and (attention_mask is None or attention_mask.ndim == 2):
+        # calculate RoPE index once per generation in the pre-fill stage only
+        if (
+            (cache_position is not None and cache_position[0] == 0)
+            or self.rope_deltas is None
+            or (past_key_values is None or past_key_values.get_seq_length() == 0)  # type: ignore
+        ):
+            position_ids, rope_deltas = self.get_rope_index(
+                input_ids, image_grid_thw, video_grid_thw, attention_mask
+            )
+            self.rope_deltas = rope_deltas
+        # then use the prev pre-calculated rope-deltas to get the correct position ids
+        else:
+            batch_size, seq_length, _ = inputs_embeds.shape
+            delta = (
+                cache_position[0] + self.rope_deltas
+                if cache_position is not None
+                else 0
+            )
+            position_ids = torch.arange(seq_length, device=inputs_embeds.device)  # type: ignore
+            position_ids = position_ids.view(1, -1).expand(batch_size, -1)  # type: ignore
+            if cache_position is not None:  # otherwise `deltas` is an int `0`
+                delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)  # type: ignore
+                delta = delta.to(position_ids.device)  # type: ignore
+            position_ids = position_ids.add(delta)  # type: ignore
+            position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)  # type: ignore
+
+    outputs = self.model(
+        input_ids=None,
+        position_ids=position_ids,
+        attention_mask=attention_mask,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+    )
+
+    hidden_states = outputs[0]
+    logits = None
+    loss = None
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states,
+            self.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+        )
+    else:
+        logits = self.lm_head(hidden_states)
+
+        if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return Qwen2VLCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        rope_deltas=self.rope_deltas,
+    )
+
+
+def patch_qwen2_vl(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+
+    from transformers.models.qwen2_vl import modeling_qwen2_vl
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_qwen2_vl.Qwen2VLForConditionalGeneration
+        ), f"Expected a Qwen2VLForConditionalGeneration model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
+
+        return maybe_model
+
+    modeling_qwen2_vl.Qwen2VLForConditionalGeneration.forward = cce_forward_multimodal
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen3.py

@@ -0,0 +1,35 @@
+"""Qwen3 CCE patch. The model inherits Llama's modeling code and uses the same forward method."""
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+)
+
+
+def patch_qwen3(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    from transformers.models.qwen3 import modeling_qwen3
+
+    # Set the _PATCH_OPTS in the llama patch file
+    import axolotl.integrations.cut_cross_entropy.monkeypatch.llama as llama_patch
+
+    llama_patch._PATCH_OPTS = patch_options  # pylint: disable=protected-access
+
+    from axolotl.integrations.cut_cross_entropy.monkeypatch.llama import cce_forward
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_qwen3.Qwen3ForCausalLM
+        ), f"Expected a Qwen3ForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward, maybe_model)
+        return maybe_model
+
+    modeling_qwen3.Qwen3ForCausalLM.forward = cce_forward
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen3_moe.py

@@ -0,0 +1,194 @@
+"""Qwen3 MoE CCE patch. Adapted from transformers v4.51.2"""
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+from typing import Optional, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+    apply_lce,
+)
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.qwen3_moe.modeling_qwen3_moe import (
+    _CONFIG_FOR_DOC,
+    QWEN3_MOE_INPUTS_DOCSTRING,
+    KwargsForCausalLM,
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+    load_balancing_loss_func,
+)
+from transformers.processing_utils import Unpack
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from transformers.utils.deprecation import deprecate_kwarg
+from transformers.utils.generic import can_return_tuple
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+@can_return_tuple
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(QWEN3_MOE_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def forward(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[list[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    output_router_logits: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **kwargs: Unpack[KwargsForCausalLM],
+) -> MoeCausalLMOutputWithPast:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Qwen3MoeForCausalLM
+
+    >>> model = Qwen3MoeForCausalLM.from_pretrained("Qwen/Qwen3-MoE-15B-A2B")
+    >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-MoE-15B-A2B")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_router_logits = (
+        output_router_logits
+        if output_router_logits is not None
+        else self.config.output_router_logits
+    )
+
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs: MoeModelOutputWithPast = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        output_router_logits=output_router_logits,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs.last_hidden_state
+
+    if hidden_states is None:
+        raise ValueError("hidden_states is None")
+
+    loss = None
+    logits = None
+
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = (
+        slice(-logits_to_keep, None)
+        if isinstance(logits_to_keep, int)
+        else logits_to_keep
+    )
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states[:, slice_indices, :],
+            self.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            **kwargs,
+        )
+    else:
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits,
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        if labels is not None:
+            loss += self.router_aux_loss_coef * aux_loss.to(  # type: ignore
+                loss.device  # type: ignore
+            )  # make sure to reside in the same device
+
+    return MoeCausalLMOutputWithPast(
+        loss=loss,
+        aux_loss=aux_loss,  # type: ignore
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        router_logits=outputs.router_logits,
+    )
+
+
+def patch_qwen3_moe(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+
+    from transformers.models.qwen3_moe import modeling_qwen3_moe
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_qwen3_moe.Qwen3MoeForCausalLM
+        ), f"Expected a Qwen3MoeForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(forward, maybe_model)
+
+        return maybe_model
+
+    modeling_qwen3_moe.Qwen3MoeForCausalLM.forward = forward
+    return None

src/axolotl/integrations/kd/chat_template.py

@@ -35,6 +35,9 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
         sequence_len,
         roles_to_train=None,
         train_on_eos=None,
+        train_on_eot=None,
+        eot_tokens=None,
+        split_thinking: bool | None = False,
         logprobs_field="logprobs",
         gen_temperature=1.0,
         kd_temperature=1.0,
@@ -50,6 +53,9 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
             sequence_len,
             roles_to_train=roles_to_train,
             train_on_eos=train_on_eos,
+            train_on_eot=train_on_eot,
+            eot_tokens=eot_tokens,
+            split_thinking=split_thinking,
         )
 
     @property

src/axolotl/integrations/liger/__init__.py

@@ -23,8 +23,8 @@ import logging
 import sys
 
 from axolotl.integrations.base import BasePlugin
+from axolotl.utils.distributed import is_main_process
 
-from ...utils.distributed import zero_only
 from .args import LigerArgs  # pylint: disable=unused-import. # noqa: F401
 from .utils import patch_with_compile_disable
 
@@ -85,7 +85,7 @@ class LigerPlugin(BasePlugin):
                 kwargs["geglu"] = cfg.liger_glu_activation
             elif "swiglu" in liger_fn_sig.parameters:
                 kwargs["swiglu"] = cfg.liger_glu_activation
-            with zero_only():
+            if is_main_process(use_environ=True):
                 LOG.info(
                     f"Applying LIGER to {cfg.model_config_type} with kwargs: {kwargs}"
                 )
@@ -151,6 +151,30 @@ class LigerPlugin(BasePlugin):
                 rms_norm=cfg.liger_rms_norm,
                 layer_norm=cfg.liger_layer_norm,
             )
+        elif cfg.model_config_type == "qwen3":
+            from axolotl.integrations.liger.models.qwen3 import (
+                apply_liger_kernel_to_qwen3,
+            )
+
+            apply_liger_kernel_to_qwen3(
+                cross_entropy=cfg.liger_cross_entropy,
+                fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
+                glu_activation=cfg.liger_glu_activation,
+                rms_norm=cfg.liger_rms_norm,
+                layer_norm=cfg.liger_layer_norm,
+            )
+        elif cfg.model_config_type == "qwen3_moe":
+            from axolotl.integrations.liger.models.qwen3_moe import (
+                apply_liger_kernel_to_qwen3_moe,
+            )
+
+            apply_liger_kernel_to_qwen3_moe(
+                cross_entropy=cfg.liger_cross_entropy,
+                fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
+                glu_activation=cfg.liger_glu_activation,
+                rms_norm=cfg.liger_rms_norm,
+                layer_norm=cfg.liger_layer_norm,
+            )
         else:
             logging.warning(
                 f"Unsupported model config type: {cfg.model_config_type}. Liger not applied."

src/axolotl/integrations/liger/models/qwen3.py

@@ -0,0 +1,160 @@
+"""
+Liger FLCE for Qwen3. Based on transformers v4.51.3.
+"""
+
+import sys
+from typing import Optional, Tuple, Union
+
+import torch
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+def lce_forward(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Cache] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **kwargs,
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+    """
+
+    # pylint: disable=duplicate-code
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+
+    logits = None
+    loss = None
+    # if in training mode, don't materialize logits
+    if self.training and (labels is not None):
+        loss = LigerForCausalLMLoss(
+            hidden_states=hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+
+    else:  # if in inference mode materialize logits
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+def apply_liger_kernel_to_qwen3(
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = False,
+    rms_norm: bool = False,
+    glu_activation: bool = False,
+    layer_norm: bool = False,
+    **kwargs,  # pylint: disable=unused-argument
+) -> None:
+    # pylint: disable=duplicate-code
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
+
+    Args:
+        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
+        fused_linear_cross_entropy (bool):
+            Whether to apply Liger's fused linear cross entropy loss. Default is False.
+            `cross_entropy` and `fused_linear_cross_entropy` cannot both be False.
+            If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
+        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is False.
+        glu_activation (bool): Whether to apply Liger's SwiGLU MLP. Default is False.
+        layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
+    """
+
+    import transformers.models.qwen3.modeling_qwen3  # noqa: F401  # pylint: disable=unused-import
+    from liger_kernel.transformers.functional import liger_cross_entropy
+    from liger_kernel.transformers.layer_norm import LigerLayerNorm
+    from liger_kernel.transformers.rms_norm import LigerRMSNorm
+    from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
+
+    assert not (
+        cross_entropy and fused_linear_cross_entropy
+    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+
+    modeling_qwen3 = sys.modules["transformers.models.qwen3.modeling_qwen3"]
+
+    if rms_norm:
+        modeling_qwen3.Qwen3RMSNorm = LigerRMSNorm
+
+    if glu_activation:
+        modeling_qwen3.Qwen3MLP = LigerSwiGLUMLP
+
+    if layer_norm:
+        modeling_qwen3.nn.LayerNorm = LigerLayerNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        modeling_qwen3.Qwen3ForCausalLM.forward = lce_forward

src/axolotl/integrations/liger/models/qwen3_moe.py

@@ -0,0 +1,191 @@
+"""
+Liger FLCE for Qwen3 MoE. Based on transformers v4.51.3.
+"""
+
+import sys
+from copy import deepcopy
+from typing import List, Optional, Union
+
+import torch
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast
+from transformers.models.qwen3_moe.modeling_qwen3_moe import load_balancing_loss_func
+
+
+def lce_forward(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    output_router_logits: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **kwargs,
+) -> MoeCausalLMOutputWithPast:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+    """
+
+    # pylint: disable=duplicate-code
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_router_logits = (
+        output_router_logits
+        if output_router_logits is not None
+        else self.config.output_router_logits
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        output_router_logits=output_router_logits,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+
+    logits = None
+    loss = None
+    # if in training mode, don't materialize logits
+    if self.training and (labels is not None):
+        loss = LigerForCausalLMLoss(
+            hidden_states=hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+
+    else:  # if in inference mode materialize logits
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits,
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        if labels is not None:
+            loss += self.router_aux_loss_coef * aux_loss.to(
+                loss.device
+            )  # make sure to reside in the same device
+
+    return MoeCausalLMOutputWithPast(
+        loss=loss,
+        aux_loss=aux_loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+def apply_liger_kernel_to_qwen3_moe(
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = False,
+    rms_norm: bool = False,
+    glu_activation: bool = False,
+    layer_norm: bool = False,
+    **kwargs,  # pylint: disable=unused-argument
+) -> None:
+    # pylint: disable=duplicate-code
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
+
+    Args:
+        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
+        fused_linear_cross_entropy (bool):
+            Whether to apply Liger's fused linear cross entropy loss. Default is False.
+            `cross_entropy` and `fused_linear_cross_entropy` cannot both be False.
+            If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
+        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is False.
+        glu_activation (bool): Whether to apply Liger's SwiGLU MLP. Default is False.
+        layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
+    """
+
+    import transformers.models.qwen3_moe.modeling_qwen3_moe  # noqa: F401  # pylint: disable=unused-import
+    from liger_kernel.transformers.functional import liger_cross_entropy
+    from liger_kernel.transformers.layer_norm import LigerLayerNorm
+    from liger_kernel.transformers.rms_norm import LigerRMSNorm
+    from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
+
+    assert not (
+        cross_entropy and fused_linear_cross_entropy
+    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+
+    modeling_qwen3_moe = sys.modules["transformers.models.qwen3_moe.modeling_qwen3_moe"]
+
+    if rms_norm:
+        modeling_qwen3_moe.Qwen3MoeRMSNorm = LigerRMSNorm
+
+    if glu_activation:
+
+        def _liger_swiglu_mlp_wrapper(config, intermediate_size=None, **kwargs):
+            "Accepts intermediate_size to pass to LigerSwiGLUMLP"
+            # clone config to avoid modifying the original
+            config = deepcopy(config)
+            if intermediate_size:
+                setattr(config, "intermediate_size", intermediate_size)
+            return LigerSwiGLUMLP(config, **kwargs)
+
+        modeling_qwen3_moe.Qwen3MoeMLP = _liger_swiglu_mlp_wrapper
+
+    if layer_norm:
+        modeling_qwen3_moe.nn.LayerNorm = LigerLayerNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        modeling_qwen3_moe.Qwen3MoeForCausalLM.forward = lce_forward

src/axolotl/integrations/llm_compressor/README.md

@@ -0,0 +1,108 @@
+# LLMCompressor Integration
+
+Fine-tune sparsified models in Axolotl using Neural Magic's [LLMCompressor](https://github.com/vllm-project/llm-compressor).
+
+This integration enables fine-tuning of models sparsified using LLMCompressor within the Axolotl training framework. By combining LLMCompressor's model compression capabilities with Axolotl's distributed training pipelines, users can efficiently fine-tune sparse models at scale.
+
+It uses Axolotl’s plugin system to hook into the fine-tuning flows while maintaining sparsity throughout training.
+
+---
+
+## Requirements
+
+- Axolotl with `llmcompressor` extras:
+
+  ```bash
+  pip install "axolotl[llmcompressor]"
+  ```
+
+- Requires `llmcompressor >= 0.5.1`
+
+This will install all necessary dependencies to fine-tune sparsified models using the integration.
+
+---
+
+## Usage
+
+To enable sparse fine-tuning with this integration, include the plugin in your Axolotl config:
+
+```yaml
+plugins:
+  - axolotl.integrations.llm_compressor.LLMCompressorPlugin
+
+llmcompressor:
+  recipe:
+    finetuning_stage:
+      finetuning_modifiers:
+        ConstantPruningModifier:
+          targets: [
+            're:.*q_proj.weight',
+            're:.*k_proj.weight',
+            're:.*v_proj.weight',
+            're:.*o_proj.weight',
+            're:.*gate_proj.weight',
+            're:.*up_proj.weight',
+            're:.*down_proj.weight',
+          ]
+          start: 0
+  save_compressed: true
+# ... (other training arguments)
+```
+
+This plugin **does not apply pruning or sparsification itself** — it is intended for **fine-tuning models that have already been sparsified**.
+
+Pre-sparsified checkpoints can be:
+- Generated using [LLMCompressor](https://github.com/vllm-project/llm-compressor)
+- Downloaded from [Neural Magic's Hugging Face page](https://huggingface.co/neuralmagic)
+- Any custom LLM with compatible sparsity patterns that you've created yourself
+
+To learn more about writing and customizing LLMCompressor recipes, refer to the official documentation:
+[https://github.com/vllm-project/llm-compressor/blob/main/README.md](https://github.com/vllm-project/llm-compressor/blob/main/README.md)
+
+### Storage Optimization with save_compressed
+
+Setting `save_compressed: true` in your configuration enables saving models in a compressed format, which:
+- Reduces disk space usage by approximately 40%
+- Maintains compatibility with vLLM for accelerated inference
+- Maintains compatibility with llmcompressor for further optimization (example: quantization)
+
+This option is highly recommended when working with sparse models to maximize the benefits of model compression.
+
+### Example Config
+
+See [`examples/llama-3/sparse-finetuning.yaml`](examples/llama-3/sparse-finetuning.yaml) for a complete example.
+
+---
+
+## Inference with vLLM
+
+After fine-tuning your sparse model, you can leverage vLLM for efficient inference.
+You can also use LLMCompressor to apply additional quantization to your fine-tuned
+sparse model before inference for even greater performance benefits.:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+llm = LLM("path/to/your/sparse/model")
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+For more details on vLLM's capabilities and advanced configuration options, see the [official vLLM documentation](https://docs.vllm.ai/).
+
+## Learn More
+
+For details on available sparsity and quantization schemes, fine-tuning recipes, and usage examples, visit the official LLMCompressor repository:
+
+[https://github.com/vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor)

src/axolotl/integrations/llm_compressor/__init__.py

@@ -0,0 +1,5 @@
+"""Integration entry point for the LLMCompressor plugin."""
+
+from .plugin import LLMCompressorPlugin
+
+__all__ = ["LLMCompressorPlugin"]

src/axolotl/integrations/llm_compressor/args.py

@@ -0,0 +1,40 @@
+"""
+LLMCompressor and Sparse Finetuning config models.
+"""
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+from typing_extensions import Annotated
+
+
+class CompressionArgs(BaseModel):
+    """Sparse Finetuning config for LLMCompressor."""
+
+    # Typing for recipe is set to Any due to:
+    # https://github.com/vllm-project/llm-compressor/issues/1319
+    recipe: Annotated[
+        Any,
+        Field(
+            description="The recipe containing the compression algorithms and hyperparameters to apply."
+        ),
+    ]
+
+    save_compressed: Annotated[
+        bool,
+        Field(
+            default=False,
+            description="Whether to save the compressed model after training.",
+        ),
+    ]
+
+
+class LLMCompressorArgs(BaseModel):
+    """LLMCompressor configuration BaseModel."""
+
+    llmcompressor: Annotated[
+        CompressionArgs,
+        Field(
+            description="Arguments enabling compression pathways through the LLM Compressor plugins"
+        ),
+    ]

src/axolotl/integrations/llm_compressor/plugin.py

@@ -0,0 +1,171 @@
+"""
+Sparse Finetuning plugin for Axolotl — enables handling of sparse neural networks
+by maintaining masks for zero weights during training.
+"""
+
+import logging
+from functools import wraps
+from typing import Any, Callable, Concatenate, ParamSpec, TypeVar
+
+from llmcompressor import active_session, create_session
+from llmcompressor.core import callbacks as session_callbacks
+from llmcompressor.recipe import Recipe
+from torch.nn import Module
+from transformers.trainer import Trainer
+from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState
+from transformers.training_args import TrainingArguments
+
+from axolotl.integrations.base import BasePlugin
+
+P = ParamSpec("P")  # Params for generic function signatures
+R = TypeVar("R")  # Return type for generic function signatures
+
+LOG = logging.getLogger("axolotl.integrations.llm_compressor")
+
+
+class LLMCompressorCallbackHandler(TrainerCallback):
+    """
+    Trainer callback for Sparse Finetuning.
+    Maintains sparsity patterns during training by applying masks after optimization steps,
+    ensuring zero-weight updates are canceled out.
+    """
+
+    def __init__(self, trainer: Trainer, recipe: Any):
+        """
+        Initialize the Sparse Finetuning callback handler.
+
+        Args:
+            trainer (Trainer): Huggingface Trainer instance.
+            recipe (Recipe | dict): Sparse finetuning recipe to apply.
+        """
+        super().__init__()
+        self.trainer = trainer
+        self.recipe = (
+            Recipe.model_validate(recipe) if not isinstance(recipe, Recipe) else recipe
+        )
+        self.original_compute_loss = trainer.compute_loss
+        self.trainer.compute_loss = compute_loss_wrapper(self.trainer.compute_loss)
+        create_session()
+
+    def on_train_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the beginning of training. Initializes the compression session.
+
+        Args:
+            args (TrainingArguments): Training arguments.
+            state (TrainerState): Trainer state.
+            control (TrainerControl): Trainer control.
+        """
+        super().on_train_begin(args, state, control, **kwargs)
+        self.trainer.accelerator.wait_for_everyone()
+        active_session().initialize(
+            model=self.trainer.model,
+            optimizer=self.trainer.optimizer,
+            start=state.epoch,
+            recipe=self.recipe,
+        )
+        self.trainer.accelerator.wait_for_everyone()
+
+    def on_step_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the beginning of a training step. Triggers batch_start callback.
+        """
+        super().on_step_begin(args, state, control, **kwargs)
+        session_callbacks.batch_start()
+
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the end of a training step. Triggers optimizer and batch_end callbacks.
+        """
+        super().on_step_end(args, state, control, **kwargs)
+        session_callbacks.optim_pre_step()
+        session_callbacks.optim_post_step()
+        session_callbacks.batch_end()
+
+    def on_train_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the end of training. Finalizes the compression session.
+        """
+        super().on_train_end(args, state, control, **kwargs)
+        active_session().finalize()
+        self.trainer.compute_loss_func = self.original_compute_loss
+
+
+class LLMCompressorPlugin(BasePlugin):
+    """
+    Sparse Finetuning plugin for Axolotl integration.
+    """
+
+    def get_input_args(self) -> str:
+        """
+        Returns the path to the plugin's argument definition.
+
+        Returns:
+            str: Dotted path to the LLMCompressorArgs class.
+        """
+        return "axolotl.integrations.llm_compressor.args.LLMCompressorArgs"
+
+    def add_callbacks_post_trainer(self, cfg: Any, trainer: Trainer) -> list:
+        """
+        Adds Sparse Finetuning callback to the Trainer instance.
+
+        Args:
+            cfg (Any): Configuration object containing the sparse recipe.
+            trainer (Trainer): Huggingface Trainer instance.
+
+        Returns:
+            list: List containing the configured callback instances.
+        """
+        LOG.info("Adding Sparse Finetuning callback to the trainer")
+        callback = LLMCompressorCallbackHandler(
+            trainer=trainer,
+            recipe=cfg.llmcompressor.recipe,
+        )
+        return [callback]
+
+
+def compute_loss_wrapper(
+    compute_loss_func: Callable[Concatenate[Module, P], R],
+) -> Callable[Concatenate[Module, P], R]:
+    """
+    Wraps the loss computation function to trigger the loss_calculated callback.
+
+    Args:
+        compute_loss_func (Callable): Original loss computation function.
+
+    Returns:
+        Callable: Wrapped function that also invokes the loss_calculated callback.
+    """
+
+    @wraps(compute_loss_func)
+    def compute_and_notify(model: Module, *args: P.args, **kwargs: P.kwargs) -> R:
+        loss = compute_loss_func(model, *args, **kwargs)
+        if active_session().lifecycle.initialized_ and model.training:
+            session_callbacks.loss_calculated(loss=loss)
+        return loss
+
+    return compute_and_notify

src/axolotl/integrations/llm_compressor/utils.py

@@ -0,0 +1,40 @@
+"""Utilities for llmcompressor integration with axolotl."""
+
+from typing import Union
+
+from llmcompressor.transformers.sparsification.compressed_tensors_utils import (
+    modify_save_pretrained,
+)
+from transformers import PreTrainedModel, Trainer
+
+
+def save_compressed_model(
+    model: PreTrainedModel,
+    output_dir: Union[str, bytes],
+    trainer: Trainer,
+    safe_serialization: bool = False,
+    save_compressed: bool = False,
+) -> None:
+    """
+    Synchronize processes, apply compression hooks, and save the model.
+
+    Args:
+        model (PreTrainedModel): The model to be saved.
+        output_dir (str or bytes): Path where the model files will be written.
+        trainer (Trainer): Hugging Face Trainer for process synchronization.
+        safe_serialization (bool): Use safe serialization if True.
+        save_compressed (bool): Write compressed tensors if True.
+    """
+    trainer.accelerator.wait_for_everyone()
+
+    # Only the main process writes the files
+    if not trainer.accelerator.is_main_process:
+        return
+
+    modify_save_pretrained(model)
+    model.save_pretrained(
+        output_dir,
+        safe_serialization=safe_serialization,
+        save_compressed=save_compressed,
+        skip_sparsity_compression_stats=not save_compressed,
+    )

src/axolotl/monkeypatch/attention/__init__.py

@@ -0,0 +1,19 @@
+"""
+attention module for attention monkeypatches
+"""
+
+from transformers.integrations.flash_attention import flash_attention_forward
+
+
+def patch_xformers_attn_over_fa2():
+    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+
+    from .xformers import xformers_attention_forward
+
+    ALL_ATTENTION_FUNCTIONS["flash_attention_2"] = xformers_attention_forward
+
+
+def unpatch_xformers_attn_over_fa2():
+    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+
+    ALL_ATTENTION_FUNCTIONS["flash_attention_2"] = flash_attention_forward()

src/axolotl/monkeypatch/attention/ring_attn/patch.py

@@ -12,10 +12,8 @@ import torch
 import torch.distributed as dist
 from accelerate.logging import get_logger
 
-from axolotl.logging_config import configure_logging
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
 
-configure_logging()
 LOG = get_logger(__name__)
 
 

src/axolotl/monkeypatch/attention/xformers.py

@@ -0,0 +1,160 @@
+"""
+xformers attention implementation for packing
+"""
+
+from typing import Optional
+
+import torch
+import xformers
+import xformers.ops.fmha
+from transformers.modeling_flash_attention_utils import (
+    _upad_input,
+)
+
+from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
+
+xformers_attention = xformers.ops.fmha.memory_efficient_attention
+
+
+def xformers_attention_forward(
+    module: torch.nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    dropout: float = 0.0,  # pylint: disable=unused-argument
+    scaling: Optional[float] = None,  # pylint: disable=unused-argument
+    sliding_window: Optional[int] = None,  # pylint: disable=unused-argument
+    softcap: Optional[float] = None,  # pylint: disable=unused-argument
+    cu_seq_lens_q: Optional[torch.LongTensor] = None,
+    cu_seq_lens_k: Optional[torch.LongTensor] = None,
+    max_length_q: Optional[int] = None,
+    max_length_k: Optional[int] = None,  # pylint: disable=unused-argument
+    **kwargs,  # pylint: disable=unused-argument
+):
+    # Get dimensions
+    # query: [batch, heads, seq_len, hidden_dim]
+    batch_size = query.size(0)
+    query_length = query.shape[2]
+    key_length = key.shape[2]
+
+    # Default causal mask
+    attn_bias = xformers.ops.LowerTriangularMask()
+
+    # Check if we have sliding window attention
+    has_sliding_window = sliding_window is not None and sliding_window < query_length
+
+    # Transpose dimensions for xformers (Q: [b, h, s, d] -> [b, s, h, d])
+    query = query.transpose(1, 2)
+    key = key.transpose(1, 2)
+    value = value.transpose(1, 2)
+
+    # Get GQA parameters
+    num_attention_heads = module.config.num_attention_heads
+    num_key_value_heads = module.config.num_key_value_heads
+    head_dim = query.size(-1)
+    is_gqa = num_attention_heads != num_key_value_heads
+    n_groups = num_attention_heads // num_key_value_heads if is_gqa else 1
+
+    # If position_ids is provided and check all examples do not contain only 1 sequence, If tensor in increasing
+    # then we probably have one sequence, otherwise it is packed. Additionally check we are in pre-fill/training stage.
+    # Use `flash_attn_varlen_func` to prevent cross-example attention and also allow padding free approach
+    if position_ids is not None and (
+        max_length_q is not None
+        or (query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all())
+    ):
+        if cu_seq_lens_q is None or cu_seq_lens_k is None:
+            cu_seq_lens_q = get_cu_seqlens_from_pos_ids(position_ids)[0]
+            cu_seq_lens_q = cu_seq_lens_q.squeeze()
+            seq_lengths = cu_seq_lens_q[1:] - cu_seq_lens_q[:-1]
+            attn_bias = (
+                xformers.ops.fmha.attn_bias.BlockDiagonalCausalMask.from_seqlens(
+                    q_seqlen=seq_lengths.tolist(),
+                )
+            )
+        else:
+            query = query.reshape(-1, query.size(-2), query.size(-1))
+            key = key.reshape(-1, key.size(-2), key.size(-1))
+            value = value.reshape(-1, value.size(-2), value.size(-1))
+
+        # Handle GQA
+        if is_gqa:
+            key = key.repeat_interleave(n_groups, dim=2)
+            value = value.repeat_interleave(n_groups, dim=2)
+
+    elif attention_mask is not None:
+        query, key, value, _, cu_seq_lens, _ = _upad_input(
+            query, key, value, attention_mask, query_length
+        )
+        cu_seq_lens_q, cu_seq_lens_k = cu_seq_lens
+        seq_lengths = []
+        for i in range(len(cu_seq_lens_q) - 1):
+            seq_lengths.append(cu_seq_lens_q[i + 1] - cu_seq_lens_q[i])
+        attn_bias = xformers.ops.fmha.attn_bias.BlockDiagonalCausalMask.from_seqlens(
+            q_seqlen=seq_lengths,
+            kv_seqlen=seq_lengths,
+        )
+
+        # Handle GQA
+        if is_gqa:
+            key = key.repeat_interleave(n_groups, dim=2)
+            value = value.repeat_interleave(n_groups, dim=2)
+    else:
+        # Handle Group Query Attention (GQA) using view/expand approach from reference
+        key = key.view(batch_size, key_length, num_key_value_heads, 1, head_dim)
+        value = value.view(batch_size, key_length, num_key_value_heads, 1, head_dim)
+        key = key.expand(
+            batch_size, key_length, num_key_value_heads, n_groups, head_dim
+        )
+        value = value.expand(
+            batch_size, key_length, num_key_value_heads, n_groups, head_dim
+        )
+
+        if module.training:
+            key = key.reshape(batch_size, key_length, num_attention_heads, head_dim)
+            value = value.reshape(batch_size, key_length, num_attention_heads, head_dim)
+
+            if has_sliding_window:
+                query = query.view(
+                    1, batch_size * query_length, num_attention_heads, head_dim
+                )
+                key = key.view(
+                    1, batch_size * key_length, num_attention_heads, head_dim
+                )
+                value = value.view(
+                    1, batch_size * key_length, num_attention_heads, head_dim
+                )
+        else:
+            query = query.view(
+                batch_size, query_length, num_key_value_heads, n_groups, head_dim
+            )
+
+            # If we need a sliding window attention
+            if has_sliding_window:
+                query = query.view(
+                    1,
+                    batch_size * query_length,
+                    num_key_value_heads,
+                    n_groups,
+                    head_dim,
+                )
+                key = key.view(
+                    1, batch_size * key_length, num_key_value_heads, n_groups, head_dim
+                )
+                value = value.view(
+                    1, batch_size * key_length, num_key_value_heads, n_groups, head_dim
+                )
+
+    # Run the xformers attention
+    attn_output = xformers_attention(
+        query,
+        key,
+        value,
+        attn_bias=attn_bias,
+    )
+
+    attn_output = attn_output.view(
+        batch_size, -1, attn_output.size(-2), attn_output.size(-1)
+    )
+    return attn_output, None

src/axolotl/monkeypatch/lora_kernels.py

@@ -23,22 +23,42 @@ from axolotl.utils.dict import DictDefault
 
 LOG = get_logger(__name__)
 
-ORIGINAL_QKV_CODE = """
+QKV_PATCHES = [
+    (
+        """
     query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
     key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
     value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
 """.lstrip(
-    "\n"
-)
-
-PATCHED_QKV_CODE = """
+            "\n"
+        ),
+        """
     query_states, key_states, value_states = self.apply_qkv(hidden_states)
     query_states = query_states.view(hidden_shape).transpose(1, 2)
     key_states = key_states.view(hidden_shape).transpose(1, 2)
     value_states = value_states.view(hidden_shape).transpose(1, 2)
 """.lstrip(
-    "\n"
-)
+            "\n"
+        ),
+    ),
+    (
+        """
+    query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+    key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+    value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+""".lstrip(
+            "\n"
+        ),
+        """
+    query_states, key_states, value_states = self.apply_qkv(hidden_states)
+    query_states = self.q_norm(query_states.view(hidden_shape)).transpose(1, 2)
+    key_states = self.k_norm(key_states.view(hidden_shape)).transpose(1, 2)
+    value_states = value_states.view(hidden_shape).transpose(1, 2)
+""".lstrip(
+            "\n"
+        ),
+    ),
+]
 
 ORIGINAL_O_CODE = """
     attn_output = self.o_proj(attn_output)
@@ -128,10 +148,11 @@ def get_attention_cls_from_config(cfg: DictDefault) -> Type[nn.Module]:
     try:
         # Dynamically import the module and attention class
         module_path = f"transformers.models.{model_type}.modeling_{model_type}"
-        module = __import__(
-            module_path, fromlist=[f"{model_type.capitalize()}Attention"]
+        model_cls_prefix = "".join(
+            [part.capitalize() for part in model_type.split("_")]
         )
-        attention_cls = getattr(module, f"{model_type.capitalize()}Attention")
+        module = __import__(module_path, fromlist=[f"{model_cls_prefix}Attention"])
+        attention_cls = getattr(module, f"{model_cls_prefix}Attention")
 
         return attention_cls
     except (ImportError, AttributeError) as e:
@@ -168,10 +189,18 @@ def patch_self_attn_lora(cfg: DictDefault):
     attention_cls._original_forward = self_attn_forward
     self_attn_forward, _ = detab_code(self_attn_forward)
 
-    assert ORIGINAL_QKV_CODE in self_attn_forward, "Original QKV code not found"
+    assert any(
+        qkv_options[0] in self_attn_forward for qkv_options in QKV_PATCHES
+    ), "Original QKV code not found"
     assert ORIGINAL_O_CODE in self_attn_forward, "Original O code not found"
 
-    self_attn_forward = self_attn_forward.replace(ORIGINAL_QKV_CODE, PATCHED_QKV_CODE)
+    for qkv_orig, qkv_patched in QKV_PATCHES:
+        if qkv_orig in self_attn_forward:
+            self_attn_forward = self_attn_forward.replace(
+                qkv_orig,
+                qkv_patched,
+            )
+            break
     self_attn_forward = self_attn_forward.replace(ORIGINAL_O_CODE, PATCHED_O_CODE)
     self_attn_forward = self_attn_forward.replace(
         "def forward(",

src/axolotl/monkeypatch/loss/chunked.py

@@ -0,0 +1,134 @@
+"""
+chunked ce loss
+"""
+
+from typing import List, Optional
+
+import torch
+import torch.nn.functional as F
+
+
+# copied and modified from torchtune.modules.loss.CEWithChunkedOutputLoss
+class CEWithChunkedOutputLoss(torch.nn.Module):
+    """
+    Cross-entropy with chunked outputs that saves memory by only upcasting one chunk at a time.
+
+    For more details, please refer to: https://github.com/pytorch/torchtune/pull/1390
+    """
+
+    def __init__(self, num_output_chunks: int = 8, ignore_index: int = -100):
+        super().__init__()
+        self.num_output_chunks = num_output_chunks
+        self.ignore_index = ignore_index
+
+    def compute_cross_entropy(
+        self,
+        logits: torch.Tensor,
+        labels: torch.Tensor,
+        normalize: bool = True,  # pylint: disable=unused-argument
+    ) -> torch.Tensor:
+        """
+        Upcast logits to fp32 and compute cross entropy loss.
+        """
+        return F.cross_entropy(
+            logits.float(), labels, ignore_index=self.ignore_index, reduction="sum"
+        )
+
+    def forward(
+        self, logits: List[torch.Tensor], labels: torch.Tensor, reduction="sum"
+    ) -> torch.Tensor:
+        """
+        Args:
+            logits (List[torch.Tensor]): List of chunked logits of length
+                ``self.num_output_chunks``, where each chunk has shape
+                ``(batch_size, num_tokens / num_output_chunks, vocab_size)``.
+            labels (torch.Tensor): Ground truth labels of shape ``(batch_size, num_tokens)``.
+            reduction (str): The reduction to apply to the output.
+
+        Returns:
+            torch.Tensor: Cross entropy loss of shape (1,).
+        """
+
+        total_elements = (labels != self.ignore_index).sum()
+
+        # chunk and reshape labels (bsz, num_tokens, vocab) -> [(bsz*num_tokens/num_chunks, vocab)]
+        labels = [
+            target_chunk.reshape(-1)
+            for target_chunk in labels.chunk(self.num_output_chunks, dim=1)
+        ]
+        # reshape logits [(bsz, num_tokens/num_chunks, vocab)] -> [(bsz*num_tokens/num_chunks, vocab)]
+        logits = [
+            logit_chunk.reshape(-1, logit_chunk.size(-1)) for logit_chunk in logits
+        ]
+
+        # compute one chunk at a time
+        total_loss = 0.0
+        for logits_chunk, labels_chunk in zip(logits, labels):
+            total_loss += self.compute_cross_entropy(logits_chunk, labels_chunk)
+
+        if reduction == "sum":
+            return total_loss
+        return total_loss / total_elements
+
+
+def _build_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
+    loss_fn_ce = CEWithChunkedOutputLoss(num_output_chunks, ignore_index)
+    loss_fn_ce.compute_cross_entropy = torch.compile(
+        loss_fn_ce.compute_cross_entropy, backend="inductor"
+    )
+    return loss_fn_ce
+
+
+def get_causal_lm_loss(num_output_chunks: int = 8, ignore_index: int = -100):
+    loss_fn_ce = _build_chunked_ce_loss_fn(num_output_chunks, ignore_index)
+
+    def chunked_fix_cross_entropy(
+        source,
+        target,
+        num_items_in_batch: int = None,
+        ignore_index: int = -100,
+        **kwargs,
+    ):  # pylint: disable=unused-argument
+        reduction = "sum" if num_items_in_batch is not None else "mean"
+        logit_chunks = [  # pylint: disable=unnecessary-comprehension
+            chunk for chunk in source.chunk(loss_fn_ce.num_output_chunks, dim=1)
+        ]
+        loss = loss_fn_ce(logit_chunks, target, reduction=reduction)
+        if reduction == "sum":
+            loss = loss / num_items_in_batch
+        return loss
+
+    def for_causal_lm_chunked_loss(
+        logits,
+        labels,
+        vocab_size: int = None,  # pylint: disable=unused-argument
+        num_items_in_batch: Optional[int] = None,
+        ignore_index: int = -100,
+        shift_labels: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        # skip the upcast to float since we handle that in the chunking loss
+        if shift_labels is None:
+            # Shift so that tokens < n predict n
+            labels = F.pad(labels, (0, 1), value=ignore_index)
+            shift_labels = labels[..., 1:].contiguous()
+
+        # Skip Flattening the tokens
+        # Enable model parallelism
+        shift_labels = shift_labels.to(logits.device)
+        loss = chunked_fix_cross_entropy(
+            logits, shift_labels, num_items_in_batch, ignore_index, **kwargs
+        )
+        return loss
+
+    return for_causal_lm_chunked_loss
+
+
+def patch_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
+    import transformers.loss.loss_utils
+
+    for_causal_lm_chunked_loss = get_causal_lm_loss(num_output_chunks, ignore_index)
+    transformers.loss.loss_utils.ForCausalLMLoss = for_causal_lm_chunked_loss
+    transformers.loss.loss_utils.LOSS_MAPPING["ForCausalLM"] = (
+        for_causal_lm_chunked_loss
+    )

src/axolotl/monkeypatch/multipack.py

@@ -18,6 +18,8 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "mixtral",
     "qwen2",
     "qwen2_moe",
+    "qwen3",
+    "qwen3_moe",
     "falcon",
     "phi",
     "phi3",

src/axolotl/monkeypatch/peft/utils.py

@@ -0,0 +1,78 @@
+"""
+Patch prepare_model_for_kbit_training to not upcast everything
+"""
+
+import inspect
+import logging
+
+import peft
+
+import axolotl
+from axolotl.monkeypatch.utils import detab_code
+
+LOG = logging.getLogger(__name__)
+
+ORIGINAL_PREPARE_CODE = """
+        for param in model.parameters():
+            if (
+                (param.dtype == torch.float16) or (param.dtype == torch.bfloat16)
+            ) and param.__class__.__name__ != "Params4bit":
+                param.data = param.data.to(torch.float32)
+"""
+
+PATCHED_PREPARE_CODE = """
+        for name, param in model.named_parameters():
+            if (
+                (param.dtype == torch.float16) or (param.dtype == torch.bfloat16)
+            ) and param.__class__.__name__ != "Params4bit" and "norm" in name:
+                param.data = param.data.to(torch.float32)
+"""
+
+
+def get_peft_prep_code() -> str:
+    prepare = inspect.getsource(peft.utils.other.prepare_model_for_kbit_training)
+    return prepare
+
+
+def check_peft_prep_code_is_patchable() -> bool:
+    prep_code = get_peft_prep_code()
+    prep_code, _ = detab_code(prep_code)
+    return ORIGINAL_PREPARE_CODE in prep_code
+
+
+def patch_peft_prep_code():
+    """
+    monkeypatch create_accelerator_and_postprocess so it checks for additional kwargs
+    """
+
+    try:
+        prep_code = get_peft_prep_code()
+    except OSError:
+        return
+    peft.utils.other._original_create_accelerator_and_postprocess = (  # pylint: disable=protected-access
+        prep_code
+    )
+    prep_code, _ = detab_code(prep_code)
+    if ORIGINAL_PREPARE_CODE not in prep_code:
+        return
+
+    prep_code = prep_code.replace(ORIGINAL_PREPARE_CODE, PATCHED_PREPARE_CODE)
+    prep_code = prep_code.replace(
+        "def prepare_model_for_kbit_training(",
+        "def fixed_prepare_model_for_kbit_training(",
+        1,
+    )
+
+    items_to_import = []
+    for item in dir(peft.utils.other):
+        if item in prep_code:
+            items_to_import.append(item)
+
+    exec(  # pylint: disable=exec-used  # nosec B102
+        "from peft.utils.other import (" + ", ".join(x for x in items_to_import) + ")",
+        globals(),
+    )
+    exec(prep_code, globals())  # pylint: disable=exec-used  # nosec B102
+    LOG.info("patching prepare_model_for_kbit_training to allow for overrides")
+    peft.utils.other.prepare_model_for_kbit_training = fixed_prepare_model_for_kbit_training  # pylint: disable=protected-access  # pylint: disable=undefined-variable  # noqa: F821
+    axolotl.utils.models.prepare_model_for_kbit_training = fixed_prepare_model_for_kbit_training  # pylint: disable=protected-access  # pylint: disable=undefined-variable  # noqa: F821

src/axolotl/monkeypatch/trainer/lr.py

@@ -0,0 +1,42 @@
+"""
+monkeypatch for Trainer _get_learning_rate method
+"""
+
+import logging
+
+import torch
+
+LOG = logging.getLogger(__name__)
+
+
+# TODO remove this patch once https://github.com/huggingface/transformers/pull/37881 is included in a release
+def _get_learning_rate(self):
+    if self.is_deepspeed_enabled:
+        # with deepspeed's fp16 and dynamic loss scale enabled the optimizer/scheduler steps may
+        # not run for the first few dozen steps while loss scale is too large, and thus during
+        # that time `get_last_lr` will fail if called during that warm up stage, so work around it:
+        try:
+            last_lr = self.lr_scheduler.get_last_lr()[0]
+        except AssertionError as e:
+            if "need to call step" in str(e):
+                LOG.warning(
+                    "tried to get lr value before scheduler/optimizer started stepping, returning lr=0"
+                )
+                last_lr = 0
+            else:
+                raise
+    else:
+        if isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
+            last_lr = self.optimizer.param_groups[0]["lr"]
+        else:
+            last_lr = self.lr_scheduler.get_last_lr()[0]
+
+    if torch.is_tensor(last_lr):
+        last_lr = last_lr.item()
+    return last_lr
+
+
+def patch_trainer_get_lr():
+    from transformers.trainer import Trainer
+
+    Trainer._get_learning_rate = _get_learning_rate  # pylint: disable=protected-access

src/axolotl/prompt_strategies/chat_template.py

@@ -4,7 +4,7 @@ HF Chat Templates prompt strategy
 
 import logging
 from collections import defaultdict
-from typing import Any, Dict, List, Optional, Set, Union
+from typing import Any, Dict, List, Set, Union
 
 from pydantic import BaseModel
 from transformers import ProcessorMixin
@@ -29,11 +29,12 @@ class ChatTemplatePrompter(Prompter):
         chat_template: str,
         processor=None,
         max_length=2048,
-        message_property_mappings: Optional[Dict[str, str]] = None,
-        message_field_training: Optional[str] = None,
-        message_field_training_detail: Optional[str] = None,
+        message_property_mappings: Dict[str, str] | None = None,
+        message_field_training: str | None = None,
+        message_field_training_detail: str | None = None,
         field_messages: str = "messages",
-        roles: Optional[Dict[str, List[str]]] = None,
+        field_system: str = "system",
+        roles: Dict[str, List[str]] | None = None,
         drop_system_message: bool = False,
     ):
         # check if message_property_mappings is None or empty dict
@@ -41,6 +42,7 @@ class ChatTemplatePrompter(Prompter):
             message_property_mappings = {
                 "role": "role",
                 "content": "content",
+                "reasoning_content": "reasoning_content",
             }
 
         if roles:
@@ -62,8 +64,9 @@ class ChatTemplatePrompter(Prompter):
         self.message_field_training = message_field_training
         self.message_field_training_detail = message_field_training_detail
         self.field_messages = field_messages
+        self.field_system = field_system
         self.tokenizer = tokenizer
-        self.processor: Optional[ProcessorMixin] = processor
+        self.processor: ProcessorMixin | None = processor
         self.chat_template = chat_template
         self.max_length = max_length
         self.drop_system_message = drop_system_message
@@ -220,10 +223,13 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         self,
         prompter: "ChatTemplatePrompter",
         tokenizer,
-        train_on_inputs,
-        sequence_len,
-        roles_to_train=None,
-        train_on_eos=None,
+        train_on_inputs: bool,
+        sequence_len: int,
+        roles_to_train: list[str] | None = None,
+        train_on_eos: str | None = None,
+        train_on_eot: str | None = None,
+        eot_tokens: list[str] | None = None,
+        split_thinking: bool | None = False,
     ):
         super().__init__(prompter, tokenizer, train_on_inputs, sequence_len)
         self.prompter: ChatTemplatePrompter = prompter
@@ -236,12 +242,88 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             ]
 
         self.train_on_eos = train_on_eos
+        # Backward compatibility, load from train_on_eos
+        self.train_on_eot = train_on_eot if train_on_eot is not None else train_on_eos
+
+        # Default to eos_token if eot_tokens not provided
+        self.eot_tokens = (
+            eot_tokens if eot_tokens is not None else [self.tokenizer.eos_token]
+        )
+        self.split_thinking = split_thinking
+
         self.images = "images"
 
         LOG.debug(
             f"The chat template uses the following properites on the message: {self.prompter.chat_template_msg_variables}"
         )
 
+        self._validate_eot_and_eos_tokens()
+
+    def _validate_eot_and_eos_tokens(self):
+        """
+        - Validates that EOT tokens (or eos_token) are in the chat_template
+        - Checks if EOT tokens are encoded as multiple tokens in the tokenizer.
+        - Checks for potential conflicts between train_on_eos and train_on_eot.
+        """
+        if self.prompter.chat_template is None:
+            # Usually this should not happen
+            LOG.warning(
+                "No chat template provided, skipping EOT and EOS token validation"
+            )
+            return
+
+        # If the EOT token is the same as the EOS token, we need to check differently
+        if len(self.eot_tokens) == 1 and self.eot_tokens[0] == self.tokenizer.eos_token:
+            # Check if the eos_token is in the chat_template or as a variable `eos_token`
+            # Note: we check for `eos_token` in the string, but it could possibly not be a variable
+            if (
+                self.tokenizer.eos_token not in self.prompter.chat_template
+                and "eos_token" not in self.prompter.chat_template
+            ):
+                LOG.warning(
+                    f"EOS token '{self.tokenizer.eos_token}' not found in chat_template. Please check if your template/EOS token is correct."
+                )
+            return
+
+        # Create a new list to store tokens that should be kept
+        valid_eot_tokens = []
+        for token in self.eot_tokens:
+            # Check if EOT token is in the chat_template
+            if token not in self.prompter.chat_template:
+                LOG.warning(f"EOT token '{token}' not found in chat_template.")
+                # Don't add to the valid tokens list
+                continue
+
+            valid_eot_tokens.append(token)
+
+        # Replace the original list with the filtered one
+        self.eot_tokens = valid_eot_tokens
+
+        for token in self.eot_tokens:
+            # If token in template, check if EOT token is in tokenizer and not encoded as multiple tokens
+            token_ids = self.tokenizer.encode(token, add_special_tokens=False)
+            if not token_ids:
+                raise ValueError(
+                    "EOT token encoding failed. Please check if the token is valid and can be encoded."
+                )
+            if token_ids and len(token_ids) > 1:
+                raise ValueError(
+                    f"EOT token '{token}' is encoded as multiple tokens: {token_ids}. Please add it under `tokens: ` in the config "
+                    "or (recommended) override unused added_tokens via `added_tokens_overrides: `."
+                )
+
+        # If eos_token is in eot_tokens and conflict between train_on_eos and train_on_eot, raise an error
+        if (
+            self.tokenizer.eos_token in self.eot_tokens
+            and self.train_on_eos != self.train_on_eot
+        ):
+            raise ValueError(
+                "Conflict between train_on_eos and train_on_eot. eos_token is in eot_tokens and train_on_eos != train_on_eot"
+                f"train_on_eos: {self.train_on_eos}, train_on_eot: {self.train_on_eot}"
+                f"eot_tokens: {self.eot_tokens}"
+                f"eos_token: {self.tokenizer.eos_token}"
+            )
+
     @property
     def supports_batched(self) -> bool:
         # Let calling code know we can handle lists of examples
@@ -285,6 +367,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         if (
             not self.roles_to_train
             and not self.train_on_eos
+            and not self.train_on_eot
             and not self.prompter.message_field_training  # type: ignore
             and not self.prompter.message_field_training_detail  # type: ignore
         ):
@@ -320,6 +403,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         labels = [IGNORE_TOKEN_ID] * len(input_ids)
 
         last_eos_idx = -1
+        last_eot_idx = -1
         for index, turn in enumerate(turns):
             role = turn.get("role")
             content = turn.get("content")
@@ -368,24 +452,45 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
                 LOG.debug(f"Labels after processing turn {index}: {labels}")
 
-            # Handle EOS token
-            eos_idx = self.find_first_eos_token(input_ids, start_idx=turn_end_idx)
-            if abs(eos_idx - turn_end_idx) <= 3:  # Allow for some template padding
-                last_eos_idx = eos_idx
-                if self.train_on_eos == "all" or (
-                    self.train_on_eos == "turn" and should_train
-                ):
-                    labels[eos_idx] = input_ids[eos_idx]
-                    LOG.debug(f"EOS token set for training at index {eos_idx}")
-            else:
-                LOG.debug(
-                    f"EOS token missing after turn {turn}. eos_idx: {eos_idx}, turn_end_idx: {turn_end_idx}"
-                )
+            # Handle special tokens (EOT and EOS)
+            for token_type, find_func, train_option in [
+                ("EOT", self.find_first_eot_token, self.train_on_eot),
+                ("EOS", self.find_first_eos_token, self.train_on_eos),
+            ]:
+                token_idx = find_func(input_ids, start_idx=turn_end_idx)
 
-        # Handle 'last' option for train_on_eos
-        if self.train_on_eos == "last" and last_eos_idx != -1:
-            labels[last_eos_idx] = input_ids[last_eos_idx]
-            LOG.debug(f"Last EOS token set for training at index {last_eos_idx}")
+                if (
+                    token_idx != -1 and abs(token_idx - turn_end_idx) <= 3
+                ):  # Allow for some template padding
+                    # Update the last token index
+                    if token_type == "EOT":  # nosec B105
+                        last_eot_idx = token_idx
+                    else:
+                        last_eos_idx = token_idx
+
+                    # Set labels if needed for this turn
+                    if train_option == "all" or (
+                        train_option == "turn" and should_train
+                    ):
+                        labels[token_idx] = input_ids[token_idx]
+                        LOG.debug(
+                            f"{token_type} token set for training at index {token_idx}"
+                        )
+                else:
+                    LOG.debug(
+                        f"{token_type} token missing after turn {turn}. {token_type.lower()}_idx: {token_idx}, turn_end_idx: {turn_end_idx}"
+                    )
+
+        # Handle 'last' option for special tokens
+        for token_type, last_idx, train_option in [
+            ("EOT", last_eot_idx, self.train_on_eot),
+            ("EOS", last_eos_idx, self.train_on_eos),
+        ]:
+            if train_option == "last" and last_idx != -1:
+                labels[last_idx] = input_ids[last_idx]
+                LOG.debug(
+                    f"Last {token_type} token set for training at index {last_idx}"
+                )
 
         LOG.debug(f"Final labels: {labels}")
 
@@ -402,6 +507,25 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 return i
         return -1
 
+    def find_first_eot_token(self, input_ids, start_idx):
+        """Find the first EOT token in the input_ids starting from start_idx."""
+        # Get token IDs for all EOT tokens
+        eot_token_ids = []
+        for token in self.eot_tokens:
+            token_ids = self.tokenizer.encode(token, add_special_tokens=False)
+            if len(token_ids) != 1:
+                raise ValueError(
+                    f"EOT token '{token}' is encoded as multiple tokens: {token_ids}. Please add it under `tokens: ` in the config."
+                )
+
+            eot_token_ids.append(token_ids[0])  # Use the last token ID if multiple
+
+        # Search for any of the EOT token IDs
+        for i in range(start_idx, len(input_ids)):
+            if input_ids[i] in eot_token_ids:
+                return i
+        return -1
+
     def find_turn(self, turns: list[dict], turn_idx: int):
         """
         Locate the starting and ending indices of the specified turn in a conversation.
@@ -488,6 +612,17 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
     def get_conversation_thread(self, prompt):
         turns = []
+
+        possible_sys_turn = self.transform_message(
+            prompt[self.prompter.field_messages][0]
+        )
+        if (
+            possible_sys_turn["role"] != "system"
+            and self.prompter.field_system in prompt
+        ):
+            turn = {"role": "system", "content": prompt[self.prompter.field_system]}
+            turns.append(turn)
+
         for message in prompt[self.prompter.field_messages]:
             transformed_message = self.transform_message(message)
 
@@ -523,6 +658,52 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 transformed_message["role"], transformed_message["role"]
             )
 
+        # TODO handle reasoning_content with split_thinking
+        # if the role is assistant that we want to use reasoning_content
+        if self.split_thinking and transformed_message["role"] == "assistant":
+            content = transformed_message["content"]
+            thinking_pairs = [
+                ("<think>", "</think>"),
+                ("<reasoning>", "</reasoning>"),
+                ("<|begin_of_thought|>", "<|end_of_thought|>"),
+            ]
+            content_pairs = [("<|begin_of_solution|>", "<|end_of_solution|>")]
+            for tpair in thinking_pairs:
+                # check if the thinking pair is in the content
+                if tpair[0] in content and tpair[1] in content:
+                    # find the start and end index of the thinking pair
+                    t_start_idx = content.find(tpair[0])
+                    t_end_idx = content.find(tpair[1])
+
+                    # get the thinking content
+                    thinking_content = content[t_start_idx + len(tpair[0]) : t_end_idx]
+                    transformed_message["reasoning_content"] = thinking_content.strip()
+
+                    # take remainder of the content
+                    # strip whitespace from beginning of the remainder (thinking tokens)
+                    remainder = content[t_end_idx + len(tpair[1]) :].lstrip()
+
+                    # check if the content pair is in the remainder
+                    cpair_found = False
+                    for cpair in content_pairs:
+                        if cpair[0] in remainder and cpair[1] in remainder:
+                            # find the start and end index of the content pair
+                            c_start_idx = remainder.find(cpair[0])
+                            c_end_idx = remainder.find(cpair[1])
+
+                            # get the content content
+                            content_content = remainder[
+                                c_start_idx + len(cpair[0]) : c_end_idx
+                            ]
+                            transformed_message["content"] = content_content.strip()
+                            cpair_found = True
+                            break
+
+                    # else, the content is the remainder
+                    if not cpair_found:
+                        transformed_message["content"] = remainder
+                    break
+
         # Determine which keys in the original message were not mapped
         mapped_values = set(self.prompter.message_property_mappings.values())
         remaining_keys = set(message) - mapped_values
@@ -555,13 +736,16 @@ class StrategyLoader:
             "sequence_len": cfg.sequence_len,
             "roles_to_train": ds_cfg.get("roles_to_train", ["assistant"]),
             "train_on_eos": ds_cfg.get("train_on_eos", "turn"),
+            "train_on_eot": ds_cfg.get("train_on_eot", None),
+            "eot_tokens": cfg.get("eot_tokens", None),  # loads from cfg, not ds_cfg
+            "split_thinking": ds_cfg.get("split_thinking", False),
         }
 
     def __call__(
         self,
         tokenizer,
         cfg,
-        ds_cfg: Optional[Union[Dict[str, Any], DatasetConfig]] = None,
+        ds_cfg: Union[Dict[str, Any], DatasetConfig] | None = None,
         processor=None,
     ):
         if ds_cfg is None:

src/axolotl/train.py

@@ -21,6 +21,7 @@ from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 from transformers.trainer import Trainer
 
+from axolotl.cli.art import print_axolotl_text_art
 from axolotl.common.datasets import TrainDatasetMeta
 from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
@@ -29,7 +30,7 @@ from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuil
 from axolotl.core.trainers.mixins.sequence_parallel import (
     SequenceParallelContextManager,
 )
-from axolotl.logging_config import configure_logging
+from axolotl.integrations.base import PluginManager
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
 from axolotl.utils.freeze import freeze_layers_except
@@ -41,7 +42,6 @@ try:
 except ImportError:
     BetterTransformer = None
 
-configure_logging()
 LOG = get_logger(__name__)
 
 
@@ -295,8 +295,23 @@ def save_trained_model(
             trainer.model.save_pretrained(
                 cfg.output_dir, safe_serialization=safe_serialization
             )
+
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
+    if hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
+        # TODO: add integration support so this can be implemented completely within the plugin
+        from axolotl.integrations.llm_compressor.utils import (
+            save_compressed_model,
+        )
+
+        save_compressed_model(
+            model=model,
+            output_dir=cfg.output_dir,
+            trainer=trainer,
+            safe_serialization=safe_serialization,
+            save_compressed=cfg.llmcompressor.save_compressed,
+        )
+
 
 def create_model_card(cfg: DictDefault, trainer: Trainer):
     """
@@ -502,6 +517,8 @@ def train(
     Returns:
         Tuple of (model, tokenizer) after training
     """
+    print_axolotl_text_art()
+
     # Setup model, tokenizer, (causal or RLHF) trainer, etc.
     (
         trainer,
@@ -533,4 +550,7 @@ def train(
     if not cfg.use_ray:
         cleanup_distributed()
 
+    plugin_manager = PluginManager.get_instance()
+    plugin_manager.post_train(cfg, model)
+
     return model, tokenizer, trainer

src/axolotl/utils/__init__.py

@@ -43,3 +43,12 @@ def set_pytorch_cuda_alloc_conf():
             os.environ["PYTORCH_CUDA_ALLOC_CONF"] = (
                 "expandable_segments:True,roundup_power2_divisions:16"
             )
+
+
+def patch_optimized_env():
+    """
+    Patch environment variables to improve VRAM usage and increase download speed
+    """
+    if os.getenv("HF_HUB_ENABLE_HF_TRANSFER") is None:
+        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+    set_pytorch_cuda_alloc_conf()

src/axolotl/utils/callbacks/__init__.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import gc
+import json
 import logging
 import os
 import traceback
@@ -808,11 +809,44 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
                     artifact.add_file(temp_file.name)
                     wandb.log_artifact(artifact)
                     wandb.save(temp_file.name)
-                LOG.info(
-                    "The Axolotl config has been saved to the WandB run under files."
-                )
+                    LOG.info(
+                        "The Axolotl config has been saved to the WandB run under files."
+                    )
             except (FileNotFoundError, ConnectionError) as err:
                 LOG.warning(f"Error while saving Axolotl config to WandB: {err}")
+
+            if args.deepspeed:
+                try:
+                    # sync config to top level in run, cannot delete file right away because wandb schedules it to be synced even w/policy = 'now', so let OS delete it later.
+                    with NamedTemporaryFile(
+                        mode="w",
+                        delete=False,
+                        suffix=".json",
+                        prefix="deepspeed_config_",
+                    ) as temp_file:
+                        skip_upload = False
+                        if isinstance(args.deepspeed, dict):
+                            json.dump(args.deepspeed, temp_file, indent=4)
+                        elif isinstance(args.deepspeed, str) and os.path.exists(
+                            args.deepspeed
+                        ):
+                            copyfile(args.deepspeed, temp_file.name)
+                        else:
+                            skip_upload = True
+                        if not skip_upload:
+                            artifact = wandb.Artifact(
+                                f"deepspeed-config-{wandb.run.id}",
+                                type="deepspeed-config",
+                            )
+                            artifact.add_file(temp_file.name)
+                            wandb.log_artifact(artifact)
+                            wandb.save(temp_file.name)
+                            LOG.info(
+                                "The DeepSpeed config has been saved to the WandB run under files."
+                            )
+                except (FileNotFoundError, ConnectionError) as err:
+                    LOG.warning(f"Error while saving DeepSpeed config to WandB: {err}")
+
         return control
 
 

src/axolotl/utils/config/__init__.py

@@ -59,7 +59,7 @@ def choose_device(cfg):
 
 def resolve_dtype(cfg):
     if (
-        cfg.bf16 == "auto" and not cfg.use_ray
+        not cfg.fp16 and cfg.bf16 == "auto" and not cfg.use_ray
     ):  # if we use ray we want to defer this check to the worker node
         if is_torch_bf16_gpu_available():
             LOG.debug("bf16 support detected, enabling for this configuration.")
@@ -67,9 +67,12 @@ def resolve_dtype(cfg):
         else:
             LOG.debug("bf16 support not detected, disabling for this configuration.")
             cfg.bf16 = False
-            if cfg.fp16 is None:
+            if cfg.fp16 is None and not cfg.float16:
                 cfg.fp16 = True
 
+    if cfg.fp16 and cfg.bf16 == "auto":
+        cfg.bf16 = False
+
     if cfg.device == "mps":
         cfg.load_in_8bit = False
         cfg.tf32 = False

src/axolotl/utils/data/rl.py

@@ -204,7 +204,37 @@ def load_prepare_preference_datasets(cfg):
             else:
                 eval_dataset = load_split(cfg.test_datasets, cfg)
         if not eval_dataset:
-            eval_dataset = None
+            if cfg.val_set_size:
+                # ensure we end up with the same fingerprint by doing rank0 first and being able to cache
+                to_hash_train = (
+                    train_dataset._fingerprint  # pylint: disable=protected-access
+                    + "|"
+                    + str(cfg.val_set_size)
+                    + "|"
+                    + "train"
+                    + "|"
+                    + str(cfg.seed or 42)
+                )
+                to_hash_test = (
+                    train_dataset._fingerprint  # pylint: disable=protected-access
+                    + "|"
+                    + str(cfg.val_set_size)
+                    + "|"
+                    + "test"
+                    + "|"
+                    + str(cfg.seed or 42)
+                )
+                train_fingerprint = md5(to_hash_train)
+                test_fingerprint = md5(to_hash_test)
+                ds_w_test_split = train_dataset.train_test_split(
+                    test_size=cfg.val_set_size,
+                    seed=cfg.seed,
+                    shuffle=False,
+                    train_new_fingerprint=train_fingerprint,
+                    test_new_fingerprint=test_fingerprint,
+                )
+                eval_dataset = ds_w_test_split["test"]
+                train_dataset = ds_w_test_split["train"]
 
         if not train_is_preprocessed:
             _save_preprocessed_ds(cfg, cfg.datasets, train_dataset)

src/axolotl/utils/distributed.py

@@ -69,17 +69,27 @@ def barrier():
         dist.barrier()
 
 
-def is_main_process():
+def is_main_process(use_environ=False):
     """
     Check if the current process is the main process. If not in distributed mode,
     always return `True`.
+
+    Args:
+    - use_environ (bool, optional): Use environment variable to determine main process.
+
+    Returns:
+    - bool: `True` if the current process is the main process, `False` otherwise.
     """
+    if use_environ:
+        return os.environ.get("LOCAL_RANK", "0") == "0"
     if not is_distributed():
         return True
     return dist.get_rank() == 0
 
 
-def is_local_main_process():
+def is_local_main_process(use_environ=False):
+    if use_environ:
+        return os.environ.get("LOCAL_RANK", "0") == "0"
     return PartialState().is_local_main_process
 
 
@@ -99,17 +109,6 @@ def cleanup_distributed():
         torch.distributed.destroy_process_group()
 
 
-@contextmanager
-def zero_only():
-    """
-    Context manager that only runs the enclosed block on the main rank.
-    """
-    if is_main_process():
-        yield
-    else:
-        yield None
-
-
 @contextmanager
 def zero_first(is_main):
     """

src/axolotl/utils/models.py

@@ -53,6 +53,7 @@ from transformers.integrations.deepspeed import (
 )
 
 from axolotl.common.architectures import MOE_ARCH_BLOCK
+from axolotl.integrations.base import PluginManager
 from axolotl.models.mamba import fix_mamba_attn_for_loss
 from axolotl.monkeypatch.multipack import (
     SUPPORTED_MULTIPACK_MODEL_TYPES,
@@ -67,13 +68,14 @@ from axolotl.utils.distributed import (
     get_device_count,
     get_device_type,
     is_local_main_process,
-    zero_only,
+    is_main_process,
 )
 from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_offload_wrapper
 from axolotl.utils.lora_embeddings import get_linear_embedding_layers
 from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_model_quant
 
 LOG = logging.getLogger(__name__)
+PLUGIN_MANAGER = PluginManager.get_instance()
 
 MULTIMODAL_AUTO_MODEL_MAPPING = {
     "mllama": MllamaForConditionalGeneration,
@@ -139,6 +141,22 @@ def check_model_config(cfg: DictDefault, model_config: PretrainedConfig):
         hasattr(model_config, "quantization_config")
         and model_config.quantization_config
     )
+
+    # Detect compressed-tensors config
+    is_compressed_tensors_config = (
+        quant_config_exists
+        and model_config.quantization_config.get("quant_method") == "compressed-tensors"
+    )
+
+    if is_compressed_tensors_config:
+        if model_config.quantization_config.get("config_groups"):
+            LOG.warning(
+                "Found `config_groups` in a compressed-tensors config. "
+                "QAT integration with llmcompressor is not tested."
+            )
+        # Skip further quant checks for compressed-tensors
+        return
+
     quant_config_method_is_gptq = (
         quant_config_exists
         and "quant_method" in model_config.quantization_config
@@ -435,7 +453,7 @@ def load_tokenizer(cfg):
             {"additional_special_tokens": additional_special_tokens}
         )
 
-    with zero_only():
+    if is_main_process(use_environ=True):
         LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
         LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
         LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
@@ -538,11 +556,30 @@ class ModelLoader:
         self.auto_model_loader = AutoModelForCausalLM  # pylint: disable=invalid-name
 
     def apply_patches(self) -> None:
+        if self.cfg.xformers_attention and self.cfg.sample_packing:
+            from axolotl.monkeypatch.attention import patch_xformers_attn_over_fa2
+
+            patch_xformers_attn_over_fa2()
+            self.cfg.flash_attention = True
+
+        if self.cfg.chunked_cross_entropy:
+            from axolotl.monkeypatch.loss.chunked import patch_chunked_ce_loss_fn
+
+            if self.cfg.chunked_cross_entropy_num_chunks:
+                patch_chunked_ce_loss_fn(self.cfg.chunked_cross_entropy_num_chunks)
+            else:
+                patch_chunked_ce_loss_fn()
+
         if self.cfg.fsdp_config and str(self.cfg.fsdp_config.fsdp_version) == "2":
             from axolotl.monkeypatch.accelerate.fsdp2 import patch_accelerate_fsdp_utils
 
             patch_accelerate_fsdp_utils()
 
+        if self.cfg.adapter:
+            from axolotl.monkeypatch.peft.utils import patch_peft_prep_code
+
+            patch_peft_prep_code()
+
         if self.cfg.flex_attention:
             from axolotl.monkeypatch.attention.flex_attn import (
                 patch_flex_make_mask,
@@ -571,10 +608,8 @@ class ModelLoader:
             patch_gemma3conditionalgeneration_forward()
 
         # load any patches from plugins
-        from axolotl.integrations.base import PluginManager
 
-        plugin_manager = PluginManager.get_instance()
-        plugin_manager.pre_model_load(self.cfg)
+        PLUGIN_MANAGER.pre_model_load(self.cfg)
 
         # monkey patch to allow additional Accelerator init kwargs
         if self.cfg.fp8:
@@ -877,7 +912,7 @@ class ModelLoader:
                 "bnb_4bit_compute_dtype": self.cfg.torch_dtype,
                 "bnb_4bit_use_double_quant": True,
                 "bnb_4bit_quant_type": "nf4",
-                "bnb_4bit_quant_storage": torch.bfloat16,
+                "bnb_4bit_quant_storage": torch.uint8,
             }
             if self.cfg.model_config_type in ["jamba", "qwen2_moe"] and not (
                 self.cfg.deepspeed or self.cfg.fsdp
@@ -1164,7 +1199,7 @@ class ModelLoader:
                 ],
             )
 
-    def prepare_model(self, qlora_fsdp) -> None:
+    def prepare_model(self, qlora_fsdp: bool) -> None:
         skip_prepare_model_for_kbit_training = False
         if self.cfg.model_config_type == "qwen" and self.cfg.adapter == "lora":
             # Qwen doesn't play nicely with LoRA if this is enabled
@@ -1252,6 +1287,7 @@ class ModelLoader:
 
         try:
             skip_move_to_device = self.build_model(qlora_fsdp)
+            PLUGIN_MANAGER.post_model_build(self.cfg, self.model)
         except Exception as err:  # pylint: disable=broad-exception-caught
             LOG.exception(err)
             raise err
@@ -1292,7 +1328,7 @@ class ModelLoader:
 
         # make sure these are fp32 per Ramesh et al. (2021)
         embedding_modules = get_linear_embedding_layers(self.cfg.model_config_type)
-        if not self.cfg.fsdp:
+        if self.cfg.fsdp:
             # FSDP doesn't like mixed Float and BFloat16
             self.convert_embedding_modules_dtype(
                 embedding_modules,
@@ -1331,6 +1367,8 @@ class ModelLoader:
                 before_kbit_train_or_finetune=False,
             )
 
+        PLUGIN_MANAGER.pre_lora_load(self.cfg, self.model)
+
         # ---------------------------------------------------------
         #  load lora or adapter
         # ---------------------------------------------------------
@@ -1392,7 +1430,7 @@ class ModelLoader:
             gc.collect()
             torch.cuda.empty_cache()
 
-        # TODO resume_from_checkpoint handling
+        PLUGIN_MANAGER.post_model_load(self.cfg, self.model)
         return self.model, lora_config
 
 
@@ -1427,9 +1465,13 @@ def load_adapter(model, cfg, adapter, inference=False):
     if hasattr(model, "enable_input_require_grads"):
         model.enable_input_require_grads()
     if adapter in ["lora", "qlora"]:
-        return load_lora(model, cfg, inference=inference)
+        model, lora_config = load_lora(model, cfg, inference=inference)
+        PLUGIN_MANAGER.post_lora_load(cfg, model)
+        return model, lora_config
     if adapter == "llama-adapter":
-        return load_llama_adapter(model, cfg)
+        model, lora_config = load_llama_adapter(model, cfg)
+        PLUGIN_MANAGER.post_lora_load(cfg, model)
+        return model, lora_config
 
     raise NotImplementedError(f"{adapter} peft adapter not available")
 

src/axolotl/utils/samplers/multipack.py

@@ -1,10 +1,13 @@
-# pylint: skip-file
 """
-Multipack Batch Sampler
+Multipack Batch Sampler - An efficient batch sampler for packing variable-length sequences
+into fixed-capacity batches to optimize memory usage and training throughput.
 """
+
 import logging
 import math
-from typing import Any, Iterable, List, Union
+from concurrent.futures import ProcessPoolExecutor
+from multiprocessing import cpu_count
+from typing import Iterable, List, Union
 
 import numba
 import numpy as np
@@ -13,26 +16,39 @@ from torch.utils.data import BatchSampler, Sampler, SequentialSampler
 from axolotl.utils.distributed import reduce_and_broadcast
 
 LOG = logging.getLogger(__name__)
-
 LOG.setLevel(logging.INFO)
 
 
 @numba.njit
-def ffd_check(a: np.ndarray, c: int, n: int):
-    # First-fit-decreasing bin packing
-    # Check if a[] could fit in n bins with capacity c
-    # https://en.wikipedia.org/wiki/First-fit-decreasing_bin_packing
+def ffd_check(sequence_lengths: np.ndarray, bin_capacity: int, num_bins: int):
+    """
+    First-fit-decreasing bin packing algorithm check
 
-    a = np.sort(a)[::-1]
-    bins = np.full((n,), c, dtype=a.dtype)
-    for size in a:
+    Checks if sequences with the given lengths could fit in the specified number of bins
+
+    Args:
+        sequence_lengths: Array of sequence lengths
+        bin_capacity: Maximum capacity of each bin
+        num_bins: Number of bins available
+
+    Returns:
+        True if all sequences can be packed, False otherwise
+    """
+    # Sort sequence lengths in descending order for optimal packing
+    sequence_lengths = np.sort(sequence_lengths)[::-1]
+    # Initialize all bins with full capacity
+    bins = np.full((num_bins,), bin_capacity, dtype=sequence_lengths.dtype)
+
+    # Try to place each sequence in the first bin it fits
+    for size in sequence_lengths:
         not_found = True
-        for idx in range(n):
+        for idx in range(num_bins):
             if bins[idx] >= size:
                 bins[idx] -= size
                 not_found = False
                 break
 
+        # If no bin could fit this sequence, packing failed
         if not_found:
             return False
 
@@ -40,240 +56,380 @@ def ffd_check(a: np.ndarray, c: int, n: int):
 
 
 @numba.njit
-def ffd_with_result(a: np.ndarray, c: int, start_index: int):
-    # First-fit-decreasing bin packing (with result return)
+def pack_group(
+    sequence_lengths: np.ndarray,
+    group_offset: int,
+    bin_capacity: int,
+    max_bins: int,
+    bin_size: int,
+    safe_mode: bool = True,
+):
+    """
+    Pack a group of sequences into bins using First-Fit Decreasing algorithm
 
-    indices = np.argsort(a)[::-1]
-    a = a[indices]
+    Args:
+        sequence_lengths: Array of sequence lengths
+        group_offset: Offset to apply to indices when returning results
+        bin_capacity: Maximum capacity of each bin
+        max_bins: Maximum number of bins to use
+        bin_size: Maximum number of sequences per bin
+        safe_mode: If True, use a more conservative packing approach
 
-    bins: List[Any] = []
-    bins_result: List[Any] = []
-    for a_id, size in enumerate(a):
-        add_new = True
-        for idx in range(len(bins)):
-            if bins[idx] >= size:
-                bins[idx] -= size
-                bins_result[idx].append(indices[a_id] + start_index)
-                add_new = False
+    Returns:
+        List of bins, where each bin contains indices of sequences assigned to it
+    """
+    # Get sorting indices and sort lengths in descending order
+    indices = np.argsort(sequence_lengths)[::-1]
+    sorted_lengths = sequence_lengths[indices]
+
+    bins_remaining_space: list = []  # Tracks remaining capacity in each bin
+    bins_assigned_sequences: list = []  # Tracks sequence indices assigned to each bin
+
+    for seq_id, size in enumerate(sorted_lengths):
+        global_idx = indices[seq_id] + group_offset
+
+        # Try to place sequence in existing bins
+        add_new_bin = True
+        for bin_idx, _ in enumerate(bins_remaining_space):
+            if (
+                bins_remaining_space[bin_idx] >= size
+                and len(bins_assigned_sequences[bin_idx]) < bin_size
+            ):
+                bins_remaining_space[bin_idx] -= size
+                bins_assigned_sequences[bin_idx].append(global_idx)
+                add_new_bin = False
                 break
 
-        if add_new:
-            bins.append(c - size)
-            bins_result.append([indices[a_id] + start_index])
+        # Create a new bin if needed and if we haven't reached the limit
+        if add_new_bin:
+            if len(bins_remaining_space) >= max_bins and safe_mode:
+                # In safe mode, skip items that would exceed max_bins
+                continue
+            bins_remaining_space.append(bin_capacity - size)
+            bins_assigned_sequences.append([global_idx])
 
-    return bins_result
+            # Safety check to avoid infinite bins
+            if len(bins_remaining_space) > len(sequence_lengths):
+                break
+
+    return bins_assigned_sequences
 
 
-@numba.njit
-def allocate(
-    lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
+# Define a standalone function for multiprocessing
+def _process_group(args):
+    group_lengths, start_idx, bin_capacity, max_bins, bin_size, safe_mode = args
+    return pack_group(
+        group_lengths, start_idx, bin_capacity, max_bins, bin_size, safe_mode
+    )
+
+
+def pack_parallel(
+    sequence_lengths: np.ndarray,
+    bin_capacity: int,
+    group_size: int,
+    bin_size: int,
+    num_processes: int | None = None,
+    safe_mode: bool = True,
 ):
-    # Dynamic batch allocator, similar to Multifit
-    # https://en.wikipedia.org/wiki/Multifit_algorithm
-    # ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
+    """
+    Pack sequences into bins using parallel processing
 
-    s = 0
-    start_index = 0
-    result = []
+    Args:
+        sequence_lengths: Array of sequence lengths
+        bin_capacity: Maximum capacity of each bin as total number of tokens
+        group_size: Number of sequences to process in each group
+        bin_size: Maximum number of bins to use
+        num_processes: Number of parallel processes to use
+        safe_mode: If True, use a more conservative packing approach
 
-    while True:
-        # binary search [l, r)
-        left = 1
-        right = 1 + np.searchsorted(lengths_cumsum[start_index:], s + c * n, "right")
+    Returns:
+        List of bins, where each bin contains indices of sequences assigned to it
+    """
+    num_items = len(sequence_lengths)
+    if num_processes is None:
+        num_processes = max(1, min(num_items // group_size, cpu_count()))
 
-        while right - left > 1:
-            mid = (left + right) // 2
-            if ffd_check(lengths[start_index : start_index + mid], c, n):
-                left = mid
-            else:
-                right = mid
+    # Create tasks for parallel processing
+    tasks = []
+    for i in range(0, num_items, group_size):
+        group_lengths = sequence_lengths[i : i + group_size]
+        max_bins = len(group_lengths)  # Allow as many bins as items in the group
+        tasks.append((group_lengths, i, bin_capacity, max_bins, bin_size, safe_mode))
 
-        # use length l
-        batch = ffd_with_result(
-            lengths[start_index : start_index + left], c, start_index
-        )
-        assert len(batch) <= n
-        if len(batch) < n:
-            break
+    # Process groups in parallel
+    all_bins = []
+    with ProcessPoolExecutor(max_workers=num_processes) as executor:
+        for group_bins in executor.map(_process_group, tasks):
+            all_bins.extend(group_bins)
 
-        start_index += left
-        s = lengths_cumsum[start_index - 1]
-
-        # add local rank
-        result.append(batch[rank])
-
-    return result, s, len(result) * c * n
+    return all_bins
 
 
 @numba.njit
-def allocate_sequentially(lengths: np.ndarray, rank: int, c: int, n: int):
+def allocate_sequentially(
+    sequence_lengths: np.ndarray, rank: int, bin_capacity: int, num_ranks: int
+):
     """
     Sequential allocator that preserves example order
 
     Parameters:
-    - lengths: The lengths of all examples
-    - rank: The current rank (for distributed training)
-    - c: The capacity of each bin (maximum sequence length)
-    - n: Number of ranks
+        sequence_lengths: The lengths of all examples
+        rank: The current rank (for distributed training)
+        bin_capacity: The capacity of each bin (maximum sequence length)
+        num_ranks: Number of ranks (processes/GPUs)
 
     Returns:
-    - result: List of batches for the current rank
-    - total_used: Number of actual example tokens
-    - total_slots: Maximum theoretical number of example tokens (number of bins * bin capacity)
+        rank_batches: List of batches for the current rank
+        total_tokens_used: Number of actual example tokens
+        total_token_slots: Maximum theoretical number of example tokens (number of bins * bin capacity)
     """
-    result = []
-    total_used = 0
+    rank_batches = []
+    total_tokens_used = 0
 
     # First, do sequential packing into bins
     all_bins = []
-    current_bin = [0 for i in range(0)]  # numba hint
-    remaining_capacity = c
+    current_bin = []
+    remaining_capacity = bin_capacity
 
-    for idx, size in enumerate(lengths):
+    # Process each sequence in order
+    for idx, size in enumerate(sequence_lengths):
         if size <= remaining_capacity:
             # Example fits in current bin
             current_bin.append(idx)
             remaining_capacity -= size
-            total_used += size
+            total_tokens_used += size
         else:
             # Example doesn't fit, start a new bin
             if current_bin:  # Add non-empty bin to all_bins
                 all_bins.append(current_bin)
             current_bin = [idx]
-            remaining_capacity = c - size
-            total_used += size
+            remaining_capacity = bin_capacity - size
+            total_tokens_used += size
 
     # Add the last bin if not empty
     if current_bin:
         all_bins.append(current_bin)
 
-    # Assign bins to ranks - each rank gets every n-th bin
-    for bin_idx in range(rank, len(all_bins), n):
-        result.append(all_bins[bin_idx])
+    # Assign bins to ranks - each rank gets every num_ranks-th bin
+    for bin_idx in range(rank, len(all_bins), num_ranks):
+        rank_batches.append(all_bins[bin_idx])
 
-    return result, total_used, len(all_bins) * c
+    return rank_batches, total_tokens_used, len(all_bins) * bin_capacity
 
 
 class MultipackBatchSampler(BatchSampler):
-    """Batch sampler class for multipack"""
+    """
+    Batch sampler class for efficient packing of variable-length sequences
+
+    This sampler packs sequences into fixed-capacity bins (batches) to maximize
+    GPU memory utilization and training throughput by reducing padding.
+
+    It supports both parallel packing (using FFD algorithm) and
+    sequential packing (preserving original sequence order).
+    """
 
     def __init__(
         self,
         sampler: Union[Sampler[int], Iterable[int]],
-        batch_size: int,
-        batch_max_len: int,
-        lengths: np.ndarray,
-        packing_efficiency_estimate: float = 1.0,
-        drop_last: bool = False,
-        num_count_samples: int = 16,
-        sequential: bool = False,
-        **kwargs,
+        batch_size: int,  # Number of bins per batch
+        batch_max_len: int,  # Maximum sequence length (bin capacity)
+        lengths: np.ndarray,  # Sequence lengths
+        packing_efficiency_estimate: float = 1.0,  # Initial efficiency estimate
+        drop_last: bool = False,  # Whether to drop incomplete batches
+        num_count_samples: int = 16,  # Number of samples to estimate batch count
+        sequential: bool = False,  # Whether to use sequential packing
+        group_size: int = 100_000,  # Size of groups for parallel packing
+        bin_size: int = 200,  # The max number of samples that can be packed in a single bin
+        num_processes: int | None = None,  # Number of processes for parallel packing
+        safe_mode: bool = True,  # Conservative packing to prevent training instability
+        **kwargs,  # pylint: disable=unused-argument
     ):
         super().__init__(sampler, batch_size, drop_last)
         self.batch_size = batch_size
         self.batch_max_len = batch_max_len
-        self.lengths: np.ndarray = lengths
+        self.lengths = np.array(lengths, dtype=np.int32)
         self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
         self.sequential = sequential
+        self.group_size = group_size
+        self.bin_size = bin_size
+        self.num_processes = num_processes
+        self.safe_mode = safe_mode
 
         assert isinstance(self.lengths, np.ndarray)
 
         self.epoch = 0
 
-        # statistics
-        self.eff_total_used = 0
-        self.eff_total_slots = 0
+        # Efficiency statistics tracking
+        self.total_tokens_used = 0
+        self.total_token_slots = 0
 
-        # The number of times to calculate the batches to determine the minimum packed dataset length for the local rank
+        # The number of times to calculate batches to determine minimum packed dataset length
         self.num_count_samples = num_count_samples
-        # the minimum packed dataset length across all ranks determined by a gather/broadcast
+        # Minimum packed dataset length across all ranks (determined by gather/broadcast)
         self.len_across_ranks = None
 
+        # Cache for batches
+        self._batches = None
+
         if self.sequential and not isinstance(sampler, SequentialSampler):
-            LOG.warn(
+            LOG.warning(
                 "using sequential sample packing with non-sequential sampler, did you want to also enable curriculum_sampling?"
             )
 
     def set_epoch(self, epoch: int):
+        """Set the epoch number, used for reproducible shuffling across epochs"""
         self.epoch = epoch
+        self._batches = None  # Invalidate batch cache
 
     def generate_batches(self, set_stats=False):
-        indices = [idx for idx in self.sampler]
+        """
+        Generate packed batches for training
 
-        lengths = self.lengths[indices]
-        lengths_cumsum = np.cumsum(lengths)
+        Args:
+            set_stats: Whether to update efficiency statistics
 
-        if self.sequential:
-            batches, total_used, total_slots = allocate_sequentially(
-                lengths=lengths,
-                rank=0,
-                c=self.batch_max_len,
-                n=1,
-            )
-        else:
-            batches, total_used, total_slots = allocate(
-                lengths=lengths,
-                lengths_cumsum=lengths_cumsum,
-                rank=0,
-                c=self.batch_max_len,
-                n=1,
-            )
+        Returns:
+            List of batches, where each batch contains multiple bins,
+            and each bin contains multiple sequence indices
+        """
+        if self._batches is not None:
+            return self._batches
 
-        batches = [
-            [
-                [indices[b_idx] for b_idx in batch]
-                for batch in batches[i : i + self.batch_size]
-            ]
-            for i in range(0, len(batches), self.batch_size)
+        # Get indices from the sampler
+        indices = [  # pylint: disable=unnecessary-comprehension
+            idx for idx in self.sampler
         ]
 
-        # statistics
-        if set_stats:
-            self.eff_total_used += total_used
-            self.eff_total_slots += total_slots
+        # Get lengths of the selected sequences
+        lengths = self.lengths[indices]
 
+        # Pack sequences into bins using either sequential or parallel packing
+        if self.sequential:
+            bins, total_used, total_slots = allocate_sequentially(
+                lengths,
+                rank=0,
+                bin_capacity=self.batch_max_len,
+                num_ranks=1,
+            )
+        else:
+            # Use parallel packing
+            all_bins = pack_parallel(
+                lengths,
+                bin_capacity=self.batch_max_len,
+                group_size=self.group_size,
+                bin_size=self.bin_size,
+                num_processes=self.num_processes,
+                safe_mode=self.safe_mode,
+            )
+
+            # Map bin indices back to original indices
+            bins = [
+                [indices[b_idx] for b_idx in bin_indices] for bin_indices in all_bins
+            ]
+
+            # Calculate efficiency statistics
+            total_used = lengths.sum()
+            total_slots = len(all_bins) * self.batch_max_len
+
+        # Group bins into batches (each batch contains batch_size bins)
+        batches = [
+            bins[i : i + self.batch_size] for i in range(0, len(bins), self.batch_size)
+        ]
+
+        # Drop last batch if requested and it's incomplete
+        if self.drop_last and len(batches[-1]) < self.batch_size:
+            batches = batches[:-1]
+            # Adjust total_slots if we dropped a batch
+            if not self.sequential:
+                total_slots -= (self.batch_size - len(batches[-1])) * self.batch_max_len
+
+        # Update statistics if requested
+        if set_stats:
+            self.total_tokens_used += total_used
+            self.total_token_slots += total_slots
+
+        self._batches = batches
         return batches
 
     def __iter__(self):
+        """
+        Return an iterator over batches
+
+        The batches are truncated to match the minimum number of batches across all ranks
+        to ensure distributed training balance
+        """
         batches = self.generate_batches(set_stats=True)
         if self.len_across_ranks:
-            # make sure the batches we iterate over is truncated to the same min length across all ranks
+            # Truncate batches to ensure all ranks have the same number of batches
             batches = batches[: self.len_across_ranks]
         return iter(batches)
 
-    def num_batches(self):
-        batches = self.generate_batches(set_stats=True)
-        return len(batches)
-
     def efficiency(self):
-        return self.eff_total_used / self.eff_total_slots
+        """
+        Calculate the packing efficiency (ratio of tokens used to total token slots)
+        Higher is better - 1.0 would mean perfect packing with no wasted space
+        """
+        if self.total_token_slots == 0:
+            self.generate_batches(set_stats=True)
+        if self.total_token_slots == 0:
+            return 0.0
+        # Return a Python float instead of potentially a numpy float
+        return float(self.total_tokens_used / self.total_token_slots)
 
     def gather_efficiency(self):
+        """
+        Gather and synchronize packing efficiency estimates across all distributed ranks
+        Returns a conservative efficiency estimate based on the measurements
+        """
+
         def calc_sample_packing_eff_est(estimates: List[float]):
             LOG.debug(f"sample_packing_eff_est across ranks: {repr(estimates)}")
-            return math.floor(0.997 * max(estimates))
+            # Use 99.7% of max observed efficiency as a safe estimate
+            max_eff = max(float(eff) for eff in estimates)
+            return math.floor(0.997 * max_eff)
 
+        # Gather efficiency from all ranks and apply the calculation function
         sample_packing_actual_eff_all = reduce_and_broadcast(
-            lambda: self.efficiency(),  # pylint: disable=unnecessary-lambda
+            lambda: float(self.efficiency()),  # pylint: disable=unnecessary-lambda
             calc_sample_packing_eff_est,
         )
+
+        # Quantize to 0.5% intervals for stability
         sample_packing_eff_est = (
             math.ceil(sample_packing_actual_eff_all * 200.0) / 200.0
         )
         return sample_packing_eff_est
 
     def gather_len_batches(self, num):
+        """
+        Gather and synchronize batch counts across all distributed ranks
+        Returns the minimum number of batches available on any rank
+        """
+
         def calc_min_len(estimates: list[(int, float)]):
             LOG.info(f"gather_len_batches: {repr(estimates)}")
             return math.floor(min(estimates))
 
+        # Find minimum batch count across ranks to ensure balance
         min_len_batches = reduce_and_broadcast(lambda: num, calc_min_len)
         return min_len_batches
 
     def __len__(self):
-        if not self.len_across_ranks:
-            len_batches = min(
-                [self.num_batches() for _ in range(self.num_count_samples)]
+        """
+        Return the total number of batches that will be yielded by this sampler
+
+        This is calculated as the minimum number of batches available on any rank
+        to ensure balanced distributed training
+        """
+        if self._batches is None:
+            self._batches = self.generate_batches(set_stats=True)
+
+        if self.len_across_ranks is None:
+            # Sample multiple times to get stable estimate
+            len_batches = min(  # pylint: disable=consider-using-generator
+                [len(self._batches) for _ in range(self.num_count_samples)]
             )
+            # Gather minimum across all ranks
             self.len_across_ranks = self.gather_len_batches(len_batches)
+
         return self.len_across_ranks

src/axolotl/utils/schemas/config.py

@@ -242,6 +242,9 @@ class AxolotlInputConfig(
     unsloth_rms_norm: bool | None = None
     unsloth_rope: bool | None = None
 
+    chunked_cross_entropy: bool | None = None
+    chunked_cross_entropy_num_chunks: int | None = None
+
     lora_mlp_kernel: bool | None = None
     lora_qkv_kernel: bool | None = None
     lora_o_kernel: bool | None = None
@@ -309,6 +312,7 @@ class AxolotlInputConfig(
         | Annotated[str, StringConstraints(pattern="^tokenizer_default_fallback_")]
     ) | None = None
     chat_template_jinja: str | None = None
+    eot_tokens: list[str] | None = None
     default_system_message: str | None = None
 
     fix_untrained_tokens: int | list[int] | None = None
@@ -434,16 +438,6 @@ class AxolotlInputConfig(
             )
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_sample_packing_w_xformers(cls, data):
-        if data.get("sample_packing") and data.get("xformers_attention"):
-            raise ValueError(
-                "sample_packing not compatible with xformers_attention. Use flash_attention"
-            )
-
-        return data
-
     @model_validator(mode="before")
     @classmethod
     # pylint: disable=duplicate-code
@@ -511,10 +505,17 @@ class AxolotlInputConfig(
     @model_validator(mode="before")
     @classmethod
     def hint_sample_packing_padding(cls, data):
-        if data.get("sample_packing") and not data.get("pad_to_sequence_len"):
-            LOG.warning(
-                "`pad_to_sequence_len: true` is recommended when using sample_packing"
-            )
+        if data.get("sample_packing"):
+            pad_to_sequence_len = data.get("pad_to_sequence_len")
+            if pad_to_sequence_len is False:
+                LOG.warning(
+                    "`pad_to_sequence_len: true` is recommended when using sample_packing"
+                )
+            elif pad_to_sequence_len is None:
+                LOG.info(
+                    "Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing"
+                )
+                data["pad_to_sequence_len"] = True
         return data
 
     @model_validator(mode="before")
@@ -1149,6 +1150,18 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_grpo_peft_liger(cls, data):
+        if (
+            data.get("rl") == "grpo"
+            and data.get("trl", {})
+            and data.get("trl").get("use_liger_loss")
+            and data.get("adapter")
+        ):
+            raise ValueError("PEFT + GRPO + Liger is not yet supported")
+        return data
+
     @model_validator(mode="after")
     def check_sequence_parallel_degree(self):
         if not self.sequence_parallel_degree:
@@ -1314,6 +1327,57 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
                     )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_auto_enable_lora_kernels(cls, data):
+        # Only proceed if using LoRA or QLoRA adapter
+        if data.get("rl"):
+            # RL trainers not tested so don't enable kernels by default
+            return data
+        if data.get("adapter") in ["lora", "qlora"]:
+            # Skip if already set, using unsloth optimizations, or using 8-bit
+            unsloth_fields = ["unsloth_lora_mlp", "unsloth_lora_qkv", "unsloth_lora_o"]
+            kernel_fields = ["lora_mlp_kernel", "lora_qkv_kernel", "lora_o_kernel"]
+            if (
+                any(data.get(k) is not None for k in kernel_fields)
+                or any(data.get(k) for k in unsloth_fields)
+                or data.get("adapter") == "lora"
+                and data.get("load_in_8bit")
+            ):
+                return data
+
+            # Check multi-GPU compatibility
+            capabilities = data.get("capabilities")
+            is_multi_gpu = capabilities and capabilities.get("n_gpu", 0) > 1
+            is_fsdp = data.get("fsdp") is not None
+            is_fsdp2 = (
+                data.get("fsdp_config") is not None
+                and str(data.get("fsdp_config").get("fsdp_version")) == "2"
+            )
+
+            if (
+                not is_multi_gpu
+                or (is_multi_gpu and not is_fsdp)
+                or (is_multi_gpu and is_fsdp2)
+            ):
+                # Auto-enable kernels if not explicitly set by user
+                if data.get("lora_mlp_kernel") is None:
+                    data["lora_mlp_kernel"] = True
+
+                if data.get("lora_qkv_kernel") is None:
+                    data["lora_qkv_kernel"] = True
+
+                if data.get("lora_o_kernel") is None:
+                    data["lora_o_kernel"] = True
+
+                LOG.warning(
+                    "Auto-enabling LoRA kernel optimizations for faster training. "
+                    + "Please explicitly set `lora_*_kernel` config values to `false` to disable. "
+                    + "See https://docs.axolotl.ai/docs/lora_optims.html for more info."
+                )
+
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_adopt_torch_version(cls, data):

src/axolotl/utils/schemas/datasets.py

@@ -50,6 +50,7 @@ class SFTDataset(BaseModel):
     message_property_mappings: dict[str, str] | None = None
     message_field_training: str | None = None
     message_field_training_detail: str | None = None
+    split_thinking: bool | None = None
     logprobs_field: str | None = None
     temperature: float | None = None
     roles_to_train: list[str] | None = None

src/axolotl/utils/schemas/enums.py

@@ -35,6 +35,7 @@ class ChatTemplate(str, Enum):
     jamba = "jamba"  # pylint: disable=invalid-name
     jinja = "jinja"  # pylint: disable=invalid-name
     qwen_25 = "qwen_25"  # pylint: disable=invalid-name
+    qwen3 = "qwen3"  # pylint: disable=invalid-name
     tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
     exaone = "exaone"  # pylint: disable=invalid-name
     metharme = "metharme"  # pylint: disable=invalid-name

src/axolotl/utils/schemas/trl.py

@@ -67,6 +67,12 @@ class TRLConfig(BaseModel):
         default=False,
         json_schema_extra={"description": "Whether to log completions"},
     )
+    num_completions_to_print: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Number of completions to print. If `log_completions` is `True`, this will be the number of completions logged."
+        },
+    )
     sync_ref_model: bool | None = Field(
         default=False,
         json_schema_extra={
@@ -133,3 +139,25 @@ class TRLConfig(BaseModel):
             "description": "Epsilon value for clipping in the GRPO algorithm."
         },
     )
+    epsilon_high: float | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Upper-bound epsilon value for clipping in the GRPO algorithm."
+        },
+    )
+    use_liger_loss: bool | None = Field(
+        default=None,
+        json_schema_extra={"description": "Whether to use Liger loss for GRPO."},
+    )
+    loss_type: str | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Specifies the loss formulation to use. Supported values are `grpo`, `bnpo`, and `dr_grpo`."
+        },
+    )
+    mask_truncated_completions: bool = Field(
+        default=False,
+        json_schema_extra={
+            "description": "When enabled, truncated completions are excluded from the loss calculation."
+        },
+    )

src/axolotl/utils/trainer.py

@@ -597,6 +597,8 @@ def prepare_optim_env(cfg):
         os.environ["ACCELERATE_MIXED_PRECISION"] = "bf16"
     elif cfg.fp16:
         os.environ["ACCELERATE_MIXED_PRECISION"] = "fp16"
+    else:
+        os.environ["ACCELERATE_MIXED_PRECISION"] = "no"
 
 
 def prepare_opinionated_env(cfg):

tests/conftest.py

@@ -79,9 +79,9 @@ def download_smollm2_135m_model():
 
 
 @pytest.fixture(scope="session", autouse=True)
-def download_llama_68m_random_model():
+def download_smollm2_135m_gptq_model():
     # download the model
-    snapshot_download_w_retry("JackFram/llama-68m", repo_type="model")
+    snapshot_download_w_retry("lilmeaty/SmolLM2-135M-Instruct-GPTQ", repo_type="model")
 
 
 @pytest.fixture(scope="session", autouse=True)
@@ -90,6 +90,12 @@ def download_qwen_2_5_half_billion_model():
     snapshot_download_w_retry("Qwen/Qwen2.5-0.5B", repo_type="model")
 
 
+@pytest.fixture(scope="session", autouse=True)
+def download_qwen3_half_billion_model():
+    # download the model
+    snapshot_download_w_retry("Qwen/Qwen3-0.6B", repo_type="model")
+
+
 @pytest.fixture(scope="session", autouse=True)
 def download_tatsu_lab_alpaca_dataset():
     # download the dataset

tests/e2e/integrations/test_hooks.py

@@ -0,0 +1,184 @@
+"""
+e2e tests to make sure all the hooks are fired on the plugin
+"""
+
+import os
+from pathlib import Path
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.integrations.base import BasePlugin
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
+from axolotl.utils.dict import DictDefault
+
+from ..utils import check_model_output_exists
+
+
+class LogHooksPlugin(BasePlugin):
+    """
+    fixture to capture in a log file each hook that was fired
+    """
+
+    base_dir = Path("/tmp/axolotl-log-hooks")
+
+    def __init__(self):
+        self.base_dir.mkdir(parents=True, exist_ok=True)
+        try:
+            os.remove(self.base_dir.joinpath("plugin_hooks.log"))
+        except FileNotFoundError:
+            pass
+
+    def pre_model_load(self, cfg):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("pre_model_load\n")
+
+    def post_model_build(self, cfg, model):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("post_model_build\n")
+
+    def pre_lora_load(self, cfg, model):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("pre_lora_load\n")
+
+    def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("post_lora_load\n")
+
+    def post_model_load(self, cfg, model):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("post_model_load\n")
+
+    def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("create_optimizer\n")
+
+    def get_trainer_cls(self, cfg):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("get_trainer_cls\n")
+
+    def create_lr_scheduler(
+        self, cfg, trainer, optimizer, num_training_steps
+    ):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("create_lr_scheduler\n")
+
+    def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("add_callbacks_pre_trainer\n")
+        return []
+
+    def add_callbacks_post_trainer(
+        self, cfg, trainer
+    ):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("add_callbacks_post_trainer\n")
+        return []
+
+    def post_train(self, cfg, model):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("post_train\n")
+
+    def post_train_unload(self, cfg):  # pylint: disable=unused-argument
+        with open(
+            self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
+        ) as f:
+            f.write("post_train_unload\n")
+
+
+class TestPluginHooks:
+    """
+    e2e tests to make sure all the hooks are fired during the training
+    """
+
+    def test_plugin_hooks(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "plugins": [
+                    "tests.e2e.integrations.test_hooks.LogHooksPlugin",
+                ],
+                "tokenizer_type": "AutoTokenizer",
+                "sequence_len": 1024,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.02,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "max_steps": 5,
+                "flash_attention": True,
+                "bf16": "auto",
+            }
+        )
+
+        cfg = validate_config(cfg)
+        prepare_plugins(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+
+        with open(
+            "/tmp/axolotl-log-hooks" + "/plugin_hooks.log", "r", encoding="utf-8"
+        ) as f:
+            file_contents = f.readlines()
+            file_contents = "\n".join(file_contents)
+            assert "pre_model_load" in file_contents
+            assert "post_model_build" in file_contents
+            assert "pre_lora_load" in file_contents
+            assert "post_lora_load" in file_contents
+            assert "post_model_load" in file_contents
+            # assert "create_optimizer" in file_contents  # not implemented yet
+            assert "get_trainer_cls" in file_contents
+            assert "create_lr_scheduler" in file_contents
+            assert "add_callbacks_pre_trainer" in file_contents
+            assert "add_callbacks_post_trainer" in file_contents
+            assert "post_train" in file_contents
+            # assert "post_train_unload" in file_contents  # not called from test train call
+
+        try:
+            os.remove("/tmp/axolotl-log-hooks" + "/plugin_hooks.log")
+        except FileNotFoundError:
+            pass

tests/e2e/integrations/test_llm_compressor.py

@@ -0,0 +1,111 @@
+"""
+E2E smoke tests for LLMCompressorPlugin integration
+"""
+
+from pathlib import Path
+
+import pytest
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
+from axolotl.utils.dict import DictDefault
+
+from tests.e2e.utils import (
+    check_model_output_exists,
+    require_llmcompressor,
+    require_torch_2_4_1,
+)
+
+MODELS = [
+    "nm-testing/llama2.c-stories42M-pruned2.4-compressed",
+    "nm-testing/llama2.c-stories42M-gsm8k-sparse-only-compressed",
+]
+
+
+@pytest.mark.parametrize(
+    "base_model", MODELS, ids=["no-checkpoint-recipe", "with-checkpoint-recipe"]
+)
+@pytest.mark.parametrize(
+    "save_compressed", [True, False], ids=["save_compressed", "save_uncompressed"]
+)
+class TestLLMCompressorIntegration:
+    """
+    e2e tests for axolotl.integrations.llm_compressor.LLMCompressorPlugin
+    """
+
+    @require_llmcompressor
+    @require_torch_2_4_1
+    def test_llmcompressor_plugin(
+        self, temp_dir, base_model: str, save_compressed: bool
+    ):
+        from llmcompressor import active_session
+
+        # core cfg
+        cfg = DictDefault(
+            {
+                "base_model": base_model,
+                "plugins": ["axolotl.integrations.llm_compressor.LLMCompressorPlugin"],
+                "sequence_len": 1024,
+                "val_set_size": 0.05,
+                "special_tokens": {"pad_token": "<|endoftext|>"},
+                "datasets": [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
+                "num_epochs": 1,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 1e-5,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "max_steps": 5,
+                "llmcompressor": {
+                    "recipe": {
+                        "finetuning_stage": {
+                            "finetuning_modifiers": {
+                                "ConstantPruningModifier": {
+                                    "targets": [
+                                        "re:.*q_proj.weight",
+                                        "re:.*k_proj.weight",
+                                        "re:.*v_proj.weight",
+                                        "re:.*o_proj.weight",
+                                        "re:.*gate_proj.weight",
+                                        "re:.*up_proj.weight",
+                                        "re:.*down_proj.weight",
+                                    ],
+                                    "start": 0,
+                                },
+                            },
+                        },
+                    },
+                    "save_compressed": save_compressed,
+                },
+            }
+        )
+
+        prepare_plugins(cfg)
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        try:
+            train(cfg=cfg, dataset_meta=dataset_meta)
+            check_model_output_exists(temp_dir, cfg)
+            _check_llmcompressor_model_outputs(temp_dir, save_compressed)
+        finally:
+            active_session().reset()
+
+
+def _check_llmcompressor_model_outputs(temp_dir, save_compressed):
+    if save_compressed:
+        assert (Path(temp_dir) / "recipe.yaml").exists()
+
+        from compressed_tensors import ModelCompressor
+        from compressed_tensors.config import Sparse24BitMaskConfig
+
+        compressor = ModelCompressor.from_pretrained(temp_dir)
+        assert compressor is not None
+        assert isinstance(compressor.sparsity_config, Sparse24BitMaskConfig)

tests/e2e/multigpu/solo/test_grpo.py

@@ -4,11 +4,14 @@ GRPO test suite
 
 import os
 import random
+import shutil
 import subprocess  # nosec B404
 import sys
+import tempfile
 import time
 from pathlib import Path
 
+import psutil
 import pytest
 import requests
 import yaml
@@ -21,8 +24,8 @@ from tests.e2e.utils import require_vllm
 
 
 def start_vllm(
-    model: str, env: dict | None = None, wait: int | None = None, quiet=False, **kwargs
-) -> int:
+    model: str, env: dict, wait: int | None = None, quiet=False, **kwargs
+) -> subprocess.Popen:
     """
     helper function to start the VLLM server in the background, mostly for testing purposes
     """
@@ -46,10 +49,41 @@ def start_vllm(
     # print out the command to be executed
     print(" ".join(cmd))
 
+    vllm_logging_json = Path(tempfile.mkdtemp()) / "vllm_logging.json"
+    with open(vllm_logging_json, "w", encoding="utf-8") as temp_file:
+        temp_file.write(
+            """{
+  "formatters": {
+    "json": {
+      "class": "pythonjsonlogger.jsonlogger.JsonFormatter"
+    }
+  },
+  "handlers": {
+    "file": {
+      "class": "logging.FileHandler",
+      "formatter": "json",
+      "level": "DEBUG",
+      "filename": "/tmp/vllm.log",
+      "mode": "a"
+    }
+  },
+  "loggers": {
+    "vllm": {
+      "handlers": ["file"],
+      "level": "DEBUG",
+      "propagate": false
+    }
+  },
+  "version": 1
+}"""
+        )
+
+    cmd_env = env.copy()
+    cmd_env.update({"VLLM_LOGGING_CONFIG_PATH": vllm_logging_json})
     # start `trl vllm-serve` command in the background and capture the process id
     process = subprocess.Popen(  # pylint: disable=consider-using-with
         cmd,
-        env=env,
+        env=cmd_env,
         stdout=subprocess.DEVNULL if quiet else subprocess.PIPE,
         stderr=subprocess.DEVNULL if quiet else subprocess.PIPE,
     )  # nosec B603
@@ -58,32 +92,51 @@ def start_vllm(
     print(f"VLLM server process started (PID: {process.pid})")
 
     # wait until the http server is ready, even if it 404s, but timeout after 60 seconds
+    period_seconds = 5
     started = False
     if wait and host and port:
-        for _ in range(int(wait)):
+        for i in range(0, int(wait), period_seconds):
             try:
                 response = requests.get(f"http://{host}:{port}", timeout=1)
+                print(f"{i}: VLLM server (status: {response.status_code})")
                 if int(response.status_code) in [200, 404]:
                     started = True
                     break
-            except requests.exceptions.RequestException:
-                pass
+            except requests.exceptions.RequestException as exc:
+                print(f"{i}: VLLM server failed to start: {str(exc)}")
 
             # also check if the process.pid is still running
             if not process.poll() is None:
                 break
 
-            time.sleep(1)
+            time.sleep(period_seconds)
 
     if wait and not started:
         print(
             f"VLLM server process did not start within {wait} seconds. Please check your server logs."
         )
-        process.kill()
+        recursive_kill(process)
+        with open("/tmp/vllm.log", "r", encoding="utf-8") as log_file:
+            print(log_file.read())
+        shutil.rmtree("/tmp/vllm.log")
         raise RuntimeError(f"VLLM server process did not start within {wait} seconds.")
 
-    # return the process id
-    return process.pid
+    # return the process
+    return process
+
+
+def recursive_kill(process: subprocess.Popen):
+    """
+    Recursively kill a process and its children
+    """
+    process = psutil.Process(process.pid)
+    for child in psutil.Process(process.pid).children(recursive=True):
+        child.terminate()
+        child.kill()
+        os.kill(child.pid, 9)
+    process.terminate()
+    process.kill()
+    os.kill(process.pid, 9)
 
 
 class TestGRPO:
@@ -174,16 +227,17 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
 
         current_env = os.environ.copy()
         env = {
-            "NCCL_P2P_LEVEL": "LOC",
+            "NCCL_P2P_LEVEL": "NVL",
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
-            "VLLM_USE_V1": "0",
+            "VLLM_DISABLE_COMPILE_CACHE": "1",
+            # "VLLM_USE_V1": "0",
         }
-        vllm_process_id = start_vllm(
+        vllm_process = start_vllm(
             cfg.base_model,
             env=env,
             quiet=True,
-            wait=120,
+            wait=300,
             gpu_memory_utilization=0.15,
             max_model_len=cfg.vllm.max_model_len,
             enable_prefix_caching=cfg.vllm.enable_prefix_caching,
@@ -202,10 +256,14 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                     "--main-process-port",
                     f"{get_torch_dist_unique_port()}",
                 ],
-                env={"NCCL_P2P_LEVEL": "LOC", "NCCL_DEBUG": "INFO", **current_env},
+                env={
+                    "NCCL_P2P_LEVEL": "NVL",
+                    "NCCL_DEBUG": "INFO",
+                    **current_env,
+                },
             )
         finally:
-            os.kill(vllm_process_id, 9)
+            recursive_kill(vllm_process)
 
     @pytest.mark.parametrize(
         "num_gpus",
@@ -262,16 +320,17 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
 
         current_env = os.environ.copy()
         env = {
-            "NCCL_P2P_LEVEL": "LOC",  # nccl can be brittle, assume P2P isn't reliable
+            "NCCL_P2P_LEVEL": "NVL",  # nccl can be brittle, assume P2P isn't reliable
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
-            "VLLM_USE_V1": "0",
+            "VLLM_DISABLE_COMPILE_CACHE": "1",
+            # "VLLM_USE_V1": "0",
         }
-        vllm_process_id = start_vllm(
+        vllm_process = start_vllm(
             cfg.base_model,
             env=env,
             quiet=True,
-            wait=120,
+            wait=300,
             gpu_memory_utilization=0.15,
             max_model_len=cfg.vllm.max_model_len,
             enable_prefix_caching=cfg.vllm.enable_prefix_caching,
@@ -290,7 +349,11 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                     "--main-process-port",
                     f"{get_torch_dist_unique_port()}",
                 ],
-                env={"NCCL_P2P_LEVEL": "LOC", "NCCL_DEBUG": "INFO", **current_env},
+                env={
+                    "NCCL_P2P_LEVEL": "NVL",
+                    "NCCL_DEBUG": "INFO",
+                    **current_env,
+                },
             )
         finally:
-            os.kill(vllm_process_id, 9)
+            recursive_kill(vllm_process)

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -2,14 +2,19 @@
 
 # pylint: disable=redefined-outer-name
 
+from pathlib import Path
+
 import pytest
 import torch
+import yaml
 from accelerate.state import PartialState
 from peft import PeftModelForCausalLM, get_peft_config
 from transformers import AutoModelForCausalLM, LlamaForCausalLM
 from transformers.models.llama.configuration_llama import LlamaConfig
 from transformers.models.llama.modeling_llama import LlamaAttention
+from transformers.models.qwen3_moe.modeling_qwen3_moe import Qwen3MoeAttention
 
+from axolotl.cli.config import load_cfg
 from axolotl.kernels.lora import (
     apply_lora_mlp_geglu,
     apply_lora_mlp_swiglu,
@@ -66,29 +71,36 @@ def small_llama_model():
     return LlamaForCausalLM(LlamaConfig(**config))
 
 
-def test_attention_patching_integration():
+@pytest.mark.parametrize(
+    "model_name,attention_cls",
+    [
+        ("HuggingFaceTB/SmolLM2-135M", LlamaAttention),
+        ("Qwen/Qwen3-30B-A3B", Qwen3MoeAttention),
+    ],
+)
+def test_attention_patching_integration(model_name, attention_cls):
     """Test attention patching in integration context."""
-    cfg = {"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"}
+    cfg = {"base_model": model_name}
 
     # Store the original implementation
-    original_forward = getattr(LlamaAttention, "forward")
+    original_forward = getattr(attention_cls, "forward")
 
     # Apply patch
     patch_self_attn_lora(cfg)
 
     # Get the new forward method
-    patched_forward = LlamaAttention.forward
+    patched_forward = attention_cls.forward
 
     # Check the forward method was replaced
     assert original_forward is not patched_forward
     assert patched_forward.__name__ == "axolotl_attn_forward"
 
     # Check original implementation was stored
-    assert hasattr(LlamaAttention, "_original_forward")
+    assert hasattr(attention_cls, "_original_forward")
 
     # Clean up
-    setattr(LlamaAttention, "forward", original_forward)
-    delattr(LlamaAttention, "_original_forward")
+    setattr(attention_cls, "forward", original_forward)
+    delattr(attention_cls, "_original_forward")
 
 
 def test_swiglu_mlp_integration(small_llama_model):
@@ -413,3 +425,42 @@ def test_kernel_training_integration():
     # Verify correct activation function
     layer = model.model.model.layers[0]
     assert layer.mlp.forward.__func__ is apply_lora_mlp_swiglu
+
+
+def test_kernel_training_integration_auto_enable(temp_dir):
+    """Test model loading with auto-enabled kernel patches."""
+    # Create minimal config without explicitly setting kernel options
+    cfg = DictDefault(
+        {
+            "base_model": "HuggingFaceTB/SmolLM2-135M",
+            "tokenizer_config": "HuggingFaceTB/SmolLM2-135M",
+            "learning_rate": 0.000001,
+            "datasets": [
+                {
+                    "path": "mhenrichsen/alpaca_2k_test",
+                    "type": "alpaca",
+                }
+            ],
+            "micro_batch_size": 1,
+            "gradient_accumulation_steps": 1,
+            "adapter": "lora",
+            "lora_r": 8,
+            "lora_alpha": 16,
+            "lora_dropout": 0.0,
+            "lora_target_linear": True,
+            "sequence_len": 1024,
+        }
+    )
+
+    # Write cfg to yaml file
+    path = Path(temp_dir) / "config.yaml"
+    with open(path, "w", encoding="utf-8") as fout:
+        fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+    # Load config
+    cfg = load_cfg(str(path))
+
+    # Verify kernel options were auto-enabled in the config
+    assert cfg.lora_mlp_kernel is True
+    assert cfg.lora_qkv_kernel is True
+    assert cfg.lora_o_kernel is True

tests/e2e/patched/test_4d_multipack_llama.py

@@ -28,7 +28,7 @@ class Test4dMultipackLlama(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "flash_attention": False,
                 "sdp_attention": True,
                 "sample_packing": True,
@@ -41,6 +41,9 @@ class Test4dMultipackLlama(unittest.TestCase):
                 "lora_target_linear": True,
                 "sequence_len": 1024,
                 "val_set_size": 0.02,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "datasets": [
                     {
                         "path": "mhenrichsen/alpaca_2k_test",
@@ -73,7 +76,7 @@ class Test4dMultipackLlama(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "flash_attention": False,
                 "sdp_attention": False,
                 "sample_packing": True,
@@ -86,6 +89,9 @@ class Test4dMultipackLlama(unittest.TestCase):
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
                 "val_set_size": 0.02,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "datasets": [
                     {
                         "path": "mhenrichsen/alpaca_2k_test",

tests/e2e/patched/test_fused_llama.py

@@ -32,7 +32,7 @@ class TestFusedLlama(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "flash_attention": True,
                 "pad_to_sequence_len": True,
                 "flash_attn_fuse_qkv": True,
@@ -41,9 +41,7 @@ class TestFusedLlama(unittest.TestCase):
                 "sequence_len": 1024,
                 "val_set_size": 0.02,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {

tests/e2e/patched/test_llama_s2_attention.py

@@ -31,8 +31,8 @@ class TestLlamaShiftedSparseAttention(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 16384,
                 "sample_packing": False,
                 "flash_attention": True,
@@ -44,7 +44,9 @@ class TestLlamaShiftedSparseAttention(unittest.TestCase):
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
                 "val_set_size": 0.02,
-                "special_tokens": {},
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "datasets": [
                     {
                         "path": "Yukang/LongAlpaca-12k",
@@ -78,14 +80,16 @@ class TestLlamaShiftedSparseAttention(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 16384,
                 "sample_packing": False,
                 "flash_attention": True,
                 "s2_attention": True,
                 "val_set_size": 0.02,
-                "special_tokens": {},
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "datasets": [
                     {
                         "path": "Yukang/LongAlpaca-12k",

tests/e2e/patched/test_lora_llama_multipack.py

@@ -31,8 +31,8 @@ class TestLoraLlama(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 1024,
                 "sample_packing": True,
                 "flash_attention": True,
@@ -44,9 +44,7 @@ class TestLoraLlama(unittest.TestCase):
                 "lora_target_linear": True,
                 "val_set_size": 0.2,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {
@@ -84,9 +82,9 @@ class TestLoraLlama(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "TheBlokeAI/jackfram_llama-68m-GPTQ",
+                "base_model": "lilmeaty/SmolLM2-135M-Instruct-GPTQ",
                 "model_type": "AutoModelForCausalLM",
-                "tokenizer_type": "LlamaTokenizer",
+                "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 1024,
                 "sample_packing": True,
                 "flash_attention": True,
@@ -100,9 +98,7 @@ class TestLoraLlama(unittest.TestCase):
                 "lora_target_linear": True,
                 "val_set_size": 0.02,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {

tests/e2e/test_dpo.py

@@ -31,8 +31,8 @@ class TestDPOLlamaLora(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 1024,
                 "load_in_8bit": True,
                 "adapter": "lora",
@@ -40,7 +40,9 @@ class TestDPOLlamaLora(unittest.TestCase):
                 "lora_alpha": 32,
                 "lora_dropout": 0.1,
                 "lora_target_linear": True,
-                "special_tokens": {},
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "rl": "dpo",
                 "datasets": [
                     {
@@ -77,8 +79,8 @@ class TestDPOLlamaLora(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 1024,
                 "load_in_8bit": True,
                 "adapter": "lora",
@@ -86,7 +88,9 @@ class TestDPOLlamaLora(unittest.TestCase):
                 "lora_alpha": 32,
                 "lora_dropout": 0.1,
                 "lora_target_linear": True,
-                "special_tokens": {},
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "rl": "dpo",
                 "rpo_alpha": 0.5,
                 "datasets": [
@@ -124,8 +128,8 @@ class TestDPOLlamaLora(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 1024,
                 "load_in_8bit": True,
                 "adapter": "lora",
@@ -133,7 +137,9 @@ class TestDPOLlamaLora(unittest.TestCase):
                 "lora_alpha": 32,
                 "lora_dropout": 0.1,
                 "lora_target_linear": True,
-                "special_tokens": {},
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "rl": "dpo",
                 "dpo_use_weighting": True,
                 "datasets": [
@@ -172,8 +178,8 @@ class TestDPOLlamaLora(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 1024,
                 "load_in_8bit": True,
                 "adapter": "lora",
@@ -181,7 +187,9 @@ class TestDPOLlamaLora(unittest.TestCase):
                 "lora_alpha": 32,
                 "lora_dropout": 0.1,
                 "lora_target_linear": True,
-                "special_tokens": {},
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "rl": "kto_pair",
                 "datasets": [
                     {
@@ -218,8 +226,8 @@ class TestDPOLlamaLora(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 1024,
                 "load_in_8bit": True,
                 "adapter": "lora",
@@ -227,7 +235,9 @@ class TestDPOLlamaLora(unittest.TestCase):
                 "lora_alpha": 32,
                 "lora_dropout": 0.1,
                 "lora_target_linear": True,
-                "special_tokens": {},
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "rl": "ipo",
                 "datasets": [
                     {
@@ -264,8 +274,8 @@ class TestDPOLlamaLora(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 1024,
                 "load_in_8bit": True,
                 "adapter": "lora",
@@ -273,7 +283,9 @@ class TestDPOLlamaLora(unittest.TestCase):
                 "lora_alpha": 32,
                 "lora_dropout": 0.1,
                 "lora_target_linear": True,
-                "special_tokens": {},
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "rl": "orpo",
                 "orpo_alpha": 0.1,
                 "remove_unused_columns": False,
@@ -314,7 +326,7 @@ class TestDPOLlamaLora(unittest.TestCase):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "tokenizer_type": "LlamaTokenizer",
                 "sequence_len": 1024,
                 "load_in_8bit": True,
@@ -323,7 +335,9 @@ class TestDPOLlamaLora(unittest.TestCase):
                 "lora_alpha": 32,
                 "lora_dropout": 0.1,
                 "lora_target_linear": True,
-                "special_tokens": {},
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "rl": "kto",
                 "rl_beta": 0.5,
                 "kto_desirable_weight": 1.0,

tests/e2e/test_evaluate.py

@@ -0,0 +1,65 @@
+"""E2E smoke test for evaluate CLI command"""
+
+import os
+from pathlib import Path
+
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+from transformers.testing_utils import get_torch_dist_unique_port
+
+from axolotl.utils.dict import DictDefault
+
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestE2eEvaluate:
+    """Test cases for evaluate CLI"""
+
+    def test_evaluate(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "val_set_size": 0.02,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "accelerate",
+                "launch",
+                "--num-processes",
+                "2",
+                "--main_process_port",
+                f"{get_torch_dist_unique_port()}",
+                "-m",
+                "axolotl.cli.evaluate",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )

tests/e2e/test_llama.py

@@ -26,15 +26,13 @@ class TestLlama:
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "tokenizer_type": "AutoTokenizer",
                 "trust_remote_code": True,
                 "sequence_len": 512,
                 "val_set_size": 0.02,
                 "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
+                    "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
                     {