moved some DPOTrainer args to DPOConfig for future trl release

* only run the remainder of the gpu test suite if one case passes first

* also reduce the test matrix

.github/workflows/base.yml

@@ -36,6 +36,12 @@ jobs:
             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: ""
+            python_version: "3.11"
+            pytorch: 2.5.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
         uses: actions/checkout@v3

.github/workflows/main.yml

@@ -29,6 +29,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -86,6 +91,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

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

@@ -21,10 +21,17 @@ jobs:
             pytorch: 2.3.1
             axolotl_extras:
             num_gpus: 2
-          - cuda: 121
-            cuda_version: 12.1.1
+          - cuda: 124
+            cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.3.1
+            pytorch: 2.4.1
+            axolotl_extras:
+            num_gpus: 2
+            nightly_build: "true"
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.0
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"

.github/workflows/nightlies.yml

@@ -28,6 +28,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -85,6 +90,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/pypi.yml

@@ -27,7 +27,7 @@ jobs:
         run: |
           pip3 install wheel packaging
           pip3 install -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Extract tag name
         id: tag

.github/workflows/tests-nightly.yml

@@ -25,7 +25,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.0"]
     timeout-minutes: 20
 
     steps:
@@ -47,13 +47,14 @@ jobs:
           sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt
           sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
           sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
+          sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt
 
       - name: Install dependencies
         run: |
           pip3 install --upgrade pip
           pip3 install --upgrade packaging
           pip3 install -U -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Run tests
         run: |
@@ -81,13 +82,6 @@ jobs:
             num_gpus: 1
             axolotl_extras: mamba-ssm
             nightly_build: "true"
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.11"
-            pytorch: 2.3.1
-            num_gpus: 1
-            axolotl_extras: mamba-ssm
-            nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -95,6 +89,13 @@ jobs:
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.0
+            num_gpus: 1
+            axolotl_extras:
+            nightly_build: "true"
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/tests.yml

@@ -36,7 +36,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.0"]
     timeout-minutes: 20
 
     steps:
@@ -49,16 +49,20 @@ jobs:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
+      - name: upgrade pip
+        run: |
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging setuptools wheel
+
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
+          pip3 install torch==${{ matrix.pytorch_version }}
 
       - name: Install dependencies
         run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging
+          pip3 show torch
           pip3 install -U -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Run tests
         run: |
@@ -68,29 +72,17 @@ jobs:
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
-  docker-e2e-tests:
+  docker-e2e-tests-1st:
     if: github.repository_owner == 'axolotl-ai-cloud'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
-    timeout-minutes: 60
+    timeout-minutes: 90
     needs: [pre-commit, pytest]
 
     strategy:
       fail-fast: false
       matrix:
         include:
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.10"
-            pytorch: 2.3.1
-            num_gpus: 1
-            axolotl_extras: mamba-ssm
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.11"
-            pytorch: 2.3.1
-            num_gpus: 1
-            axolotl_extras: mamba-ssm
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -119,3 +111,49 @@ jobs:
       - name: Run tests job on Modal
         run: |
           modal run cicd.tests
+
+  docker-e2e-tests:
+    if: github.repository_owner == 'axolotl-ai-cloud'
+    # this job needs to be run on self-hosted GPU runners...
+    runs-on: [self-hosted, modal]
+    timeout-minutes: 90
+    needs: [pre-commit, pytest, docker-e2e-tests-1st]
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.10"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.0
+            num_gpus: 1
+            axolotl_extras:
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+      - name: Install Modal
+        run: |
+          python -m pip install --upgrade pip
+          pip install modal==0.63.64 jinja2
+      - name: Update env vars
+        run: |
+          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
+          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
+          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
+          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
+          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+      - name: Run tests job on Modal
+        run: |
+          modal run cicd.tests

README.md

@@ -121,7 +121,7 @@ Features:
 
 Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
 
-**Requirements**: Python >=3.10 and Pytorch >=2.1.1.
+**Requirements**: Nvidia GPU (Ampere architecture or newer for `bf16` and Flash Attention), Python >=3.10 and PyTorch >=2.3.1.
 
 ```bash
 git clone https://github.com/axolotl-ai-cloud/axolotl
@@ -383,7 +383,7 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
-      # fastchat conversation (deprecation soon, use chat_template)
+      # fastchat conversation (deprecation soon, use chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template)
       # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
     - path: ...
       type: sharegpt

cicd/Dockerfile.jinja

@@ -23,11 +23,11 @@ RUN git fetch origin +$GITHUB_REF && \
     git checkout FETCH_HEAD
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN pip install causal_conv1d
 RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
         sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
         sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
+        sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
@@ -37,7 +37,7 @@ RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
     fi
 
 # So we can test the Docker image
-RUN pip install -r requirements-tests.txt
+RUN pip install -r requirements-dev.txt -r requirements-tests.txt
 
 # fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \

cicd/cicd.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 set -e
 
-pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
+pytest -n4 --ignore=tests/e2e/ /workspace/axolotl/tests/
 pytest -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
 pytest --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

cicd/multigpu.py

@@ -64,7 +64,7 @@ def run_cmd(cmd: str, run_folder: str):
 @stub.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=45 * 60,
+    timeout=60 * 60,
     cpu=8.0,
     memory=131072 * N_GPUS,
 )

cicd/tests.py

@@ -65,7 +65,7 @@ def run_cmd(cmd: str, run_folder: str):
 @stub.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=45 * 60,
+    timeout=60 * 60,
     cpu=8.0,
     memory=131072,
 )

deepspeed_configs/zero3_bf16.json

@@ -14,15 +14,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

deepspeed_configs/zero3_bf16_cpuoffload_all.json

@@ -24,15 +24,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

deepspeed_configs/zero3_bf16_cpuoffload_params.json

@@ -20,15 +20,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

devtools/dev_chat_template.yml

@@ -7,8 +7,8 @@ load_in_8bit: true
 load_in_4bit: false
 
 datasets:
-  - path: philschmid/guanaco-sharegpt-style
-    type: sharegpt
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
     shards: 10
 val_set_size: 0
 output_dir: temp_debug/axolotl_outputs/model

docker/Dockerfile

@@ -20,7 +20,6 @@ RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
 WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN pip install causal_conv1d
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \

docs/config.qmd

@@ -83,7 +83,7 @@ lora_on_cpu: true
 datasets:
   # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
   - path: vicgalle/alpaca-gpt4
-  # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
+    # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
     ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
     data_files: # Optional[str] path to source data files
@@ -124,6 +124,48 @@ datasets:
       # For `completion` datsets only, uses the provided field instead of `text` column
       field:
 
+  # Using chat template
+  - path: ...
+    # Set type to `chat_template` to use this strategy
+    type: chat_template
+    # Specify the name of the chat template to use
+    # The name of the chat template to use for training, following values are supported:
+    # - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default.
+    # - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+    # - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to if the tokenizer does not have a chat template else default to tokenizer. E.g. tokenizer_default_fallback_chatml.
+    # - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
+    chat_template: tokenizer_default
+    # Custom jinja template for chat template. This will be only used if `chat_template` is set to `jinja` or empty (in which case chat_template is automatically set to `jinja`).
+    chat_template_jinja:
+    # The key in the data example that contains the messages. Default is "messages".
+    field_messages: messages
+    # The key in the message turn that contains the role. Default is "role".
+    message_field_role: role
+    # The key in the message turn that contains the content. Default is "content".
+    message_field_content: content
+    # Optional[Dict[str, List]]. Roles mapping for the messages.
+    roles:
+      user: ["human", "user"]
+      assistant: ["gpt", "assistant", "ai"]
+      system: ["system"]
+
+    ## NOTE: Leaving the below empty will default to using the simple legacy tokenization strategy where only last message is trained on.
+
+    # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
+    roles_to_train: ["gpt", "assistant"]
+    # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
+    # - all: train on all EOS tokens
+    # - turn: train on the EOS token at the end of each trainable turn
+    # - last: train on the last EOS token in the conversation
+    train_on_eos: last
+    # 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.
+    # The value of the key is a List[Dict] containing `begin_offset` (start character index in content), `end_offset` (end character index in content), and `train` (boolean whether to train).
+    # See example at `docs/dataset-formats/conversation.qmd`
+    message_field_training_detail: train_detail
+
+
 # If false, the datasets will not be shuffled and will keep their original order in `datasets`.
 # The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
 shuffle_merged_datasets: true
@@ -141,10 +183,19 @@ test_datasets:
 
 # use RL training: 'dpo', 'ipo', 'kto'
 rl:
+# whether to perform weighting if doing DPO training. Boolean.
+dpo_use_weighting:
 
-# Saves the desired chat template to the tokenizer_config.json for easier inferencing
-# Currently supports chatml and inst (mistral/mixtral)
-chat_template: chatml
+# The name of the chat template to use for training, following values are supported:
+# - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default value.
+# - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+# - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to. E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not available in the tokenizer.
+# - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
+# The selected chat template will be saved to the tokenizer_config.json for easier inferencing
+# Note: It is recommended to set train_on_inputs to true when using a chat template that is different from the model's default chat template.
+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
 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

docs/dataset-formats/conversation.qmd

@@ -6,6 +6,8 @@ order: 3
 
 ## sharegpt
 
+UPDATE: ShareGPT is being deprecated in the next release. Please see `chat_template` section below.
+
 conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
 
 ```{.json filename="data.jsonl"}
@@ -69,3 +71,138 @@ creates a chat where bot is asked to tell a joke, then explain why the joke is f
 ```{.json filename="data.jsonl"}
 {"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
 ```
+
+
+## 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.
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"role": "...", "content": "..."}]}
+```
+
+See `config.qmd` for full configs and supported templates.
+
+### Migrating from sharegpt
+
+Most configs can be adapted as follows:
+
+```yaml
+# old
+chat_template: chatml
+datasets:
+  - path: ...
+    type: sharegpt
+    conversation: chatml
+
+# new (if using tokenizer's chat_template)
+datasets:
+  - path: ...
+    type: chat_template
+
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
+# new (if setting a new chat_template like chatml, gemma, etc)
+chat_template: chatml
+datasets:
+  - path: ...
+    type: chat_template
+
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+```
+
+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.
+
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+```
+
+2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
+
+```yaml
+chat_template: gemma # this overwrites the tokenizer's chat_template
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
+
+```yaml
+chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
+
+```yaml
+# chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
+chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|system|>' + '\n' + message['content'] + '<|end|>' + '\n'}}{% elif (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif message['role'] == 'assistant' %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}"
+
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
+
+For a data sample that looks like:
+
+```{.json filename="data.jsonl"}
+{
+  "conversations": [
+    {"from": "system", "value": "You are an AI assistant.", "train": false},
+    {"from": "human", "value": "Hello", "train": false},
+    {"from": "assistant", "value": "Hello", "train": true},
+    {"from": "human", "value": "How are you?", "train": true},
+    {
+      "from": "assistant",
+      "value": "I'm doing very well, thank you!",
+      "train_detail": [
+        {"begin_offset": 0, "end_offset": 8, "train": false},
+        {"begin_offset": 9, "end_offset": 18, "train": true},
+        {"begin_offset": 19, "end_offset": 30, "train": false},
+      ],
+    },
+    {
+        "from": "human",
+        "value": "I'm doing very well, thank you!",
+        "train": true,
+    },
+    {"from": "assistant", "value": "Hi there!", "train": true}
+  ]
+}
+```
+
+The configuration would look like:
+
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+    chat_template: tokenizer_default
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+    roles_to_train: []
+    train_on_eos: turn
+    message_field_training: train
+    message_field_training_detail: train_detail
+```
+
+Tip: It is not necessary to use both `message_field_training` and `message_field_training_detail` at a time.

docs/debugging.qmd

@@ -51,12 +51,12 @@ While debugging it's helpful to simplify your test scenario as much as possible.
 
 ### Background
 
-The below example shows how to configure VSCode to debug data preprocessing of the `sharegpt` format.  This is the format used when you have the following in your axolotl config:
+The below example shows how to configure VSCode to debug data preprocessing of the `chat_template` format.  This is the format used when you have the following in your axolotl config:
 
 ```yaml
 datasets:
-  - path: <path to your sharegpt formatted dataset> # example on HF Hub: philschmid/guanaco-sharegpt-style
-    type: sharegpt
+  - path: <path to your chat_template formatted dataset> # example on HF Hub: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
 ```
 
 >[!Important]
@@ -83,7 +83,7 @@ If you developing on a remote host, you can easily use VSCode to debug remotely.
 
 The easiest way to get started is to modify the [.vscode/launch.json](../.vscode/launch.json) file in this project.  This is just an example configuration, so you may need to modify or copy it to suit your needs.
 
-For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_sharegpt.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
+For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_chat_template.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
 
 ```jsonc
 // .vscode/launch.json
@@ -91,12 +91,12 @@ For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 acceler
     "version": "0.2.0",
     "configurations": [
         {
-            "name": "Debug axolotl prompt - sharegpt",
+            "name": "Debug axolotl prompt - chat_template",
             "type": "python",
             "module": "accelerate.commands.launch",
             "request": "launch",
             "args": [
-                "-m", "axolotl.cli.train", "dev_sharegpt.yml",
+                "-m", "axolotl.cli.train", "dev_chat_template.yml",
                 // The flags below simplify debugging by overriding the axolotl config
                 // with the debugging tips above.  Modify as needed.
                 "--dataset_processes=1",      // limits data preprocessing to one process
@@ -240,6 +240,6 @@ style="border-radius: 10px; display: block; margin: auto;" width="560" height="3
 </div>
 <br>
 
-[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/sharegpt.yml`, but this is the same thing.
+[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/chat_template.yml`, but this is the same thing.
 
 [^2]: Many of the below flags are recommended best practices by Nvidia when using nvidia-container-toolkit.  You can read more about these flags [here](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html).

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -16,7 +16,10 @@ chat_template: deepseek_v2
 datasets:
   - path: mlabonne/FineTome-100k
     type: chat_template
-    split: train
+    split: train[:20%]
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
 
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0

examples/gemma2/qlora.yml

@@ -11,8 +11,11 @@ chat_template: gemma
 datasets:
   - path: cgato/SlimOrcaDedupCleaned
     type: chat_template
-    chat_template: gemma
     drop_system_message: true
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
 val_set_size: 0.0
 output_dir: ./outputs/out
 

examples/jamba/qlora_fsdp_large.yaml

@@ -4,11 +4,15 @@ tokenizer_type: AutoTokenizer
 load_in_4bit: true
 strict: false
 use_tensorboard: true
+chat_template: jamba
 datasets:
   - path: cgato/SlimOrcaDedupCleaned
     type: chat_template
-    chat_template: jamba
     drop_system_message: true
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: jamba-large-fsdp-qlora-ft

examples/llama-3/fft-8b-liger-fsdp.yaml

@@ -14,6 +14,10 @@ datasets:
   - path: mlabonne/FineTome-100k
     type: chat_template
     split: train[:20%]
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
 output_dir: ./outputs/out

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -11,7 +11,6 @@ rl: dpo
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_dpo_test
     type: chat_template.default
-    chat_template: llama3
     field_messages: conversation
     field_chosen: chosen
     field_rejected: rejected

examples/llama-3/instruct-lora-8b.yml

@@ -10,7 +10,6 @@ chat_template: llama3
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    chat_template: llama3
     field_messages: messages
     message_field_role: role
     message_field_content: content

examples/llama-3/qlora-1b.yml

@@ -0,0 +1,77 @@
+base_model: meta-llama/Llama-3.2-1B
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|end_of_text|>"

examples/phi/lora-3.5.yaml

@@ -10,7 +10,6 @@ chat_template: phi_3
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    chat_template: phi_3
     field_messages: messages
     message_field_role: role
     message_field_content: content

requirements-dev.txt

@@ -2,3 +2,4 @@ pre-commit
 black
 mypy
 types-requests
+tbparse

requirements.txt

@@ -1,12 +1,12 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.13.2
-transformers==4.45.2
+transformers==4.46.0
 tokenizers>=0.20.1
 bitsandbytes==0.44.1
 accelerate==1.0.1
 datasets==3.0.1
-deepspeed==0.14.4
+deepspeed==0.15.3
 pydantic==2.6.3
 addict
 fire
@@ -16,7 +16,7 @@ flash-attn==2.6.3
 sentencepiece
 wandb
 einops
-xformers==0.0.28.post1
+xformers>=0.0.23.post1
 optimum==1.16.2
 hf_transfer
 colorama
@@ -43,7 +43,7 @@ s3fs>=2024.5.0
 gcsfs>=2024.5.0
 # adlfs
 
-trl==0.9.6
+trl @ git++https://github.com/huggingface/trl.git@5e90682836969310e16ed8aa711dd429f85863b7
 zstandard==0.22.0
 fastcore
 

setup.py

@@ -31,6 +31,8 @@ def parse_requirements():
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
         torchao_version = [req for req in _install_requires if "torchao" in req][0]
+        autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
+
         if "Darwin" in platform.system():
             # don't install xformers on MacOS
             _install_requires.pop(_install_requires.index(xformers_version))
@@ -50,10 +52,16 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 4):
+            if (major, minor) >= (2, 5):
+                _install_requires.pop(_install_requires.index(xformers_version))
+                _install_requires.pop(_install_requires.index(autoawq_version))
+            elif (major, minor) >= (2, 4):
                 if patch == 0:
                     _install_requires.pop(_install_requires.index(xformers_version))
                     _install_requires.append("xformers>=0.0.27")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers==0.0.28.post1")
             elif (major, minor) >= (2, 3):
                 _install_requires.pop(_install_requires.index(torchao_version))
                 if patch == 0:
@@ -73,7 +81,6 @@ def parse_requirements():
 
     except PackageNotFoundError:
         pass
-
     return _install_requires, _dependency_links
 
 
@@ -102,6 +109,7 @@ setup(
         ],
         "mamba-ssm": [
             "mamba-ssm==1.2.0.post1",
+            "causal_conv1d",
         ],
         "auto-gptq": [
             "auto-gptq==0.5.1",

src/axolotl/cli/__init__.py

@@ -30,7 +30,7 @@ from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.integrations.base import PluginManager
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template
 from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
@@ -272,7 +272,7 @@ def do_inference_gradio(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = chat_templates(cfg.chat_template)
+        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -462,7 +462,12 @@ def load_datasets(
         processor=processor,
     )
 
-    if cli_args.debug or cfg.debug:
+    if (
+        cli_args.debug
+        or cfg.debug
+        or cli_args.debug_text_only
+        or int(cli_args.debug_num_examples) > 0
+    ):
         LOG.info("check_dataset_labels...")
         check_dataset_labels(
             train_dataset.select(

src/axolotl/common/cli.py

@@ -23,7 +23,7 @@ class TrainerCliArgs:
 
     debug: bool = field(default=False)
     debug_text_only: bool = field(default=False)
-    debug_num_examples: int = field(default=5)
+    debug_num_examples: int = field(default=0)
     inference: bool = field(default=False)
     merge_lora: bool = field(default=False)
     prompter: Optional[str] = field(default=None)

src/axolotl/core/trainer_builder.py

@@ -7,6 +7,7 @@ import abc
 import gc
 import importlib
 import importlib.util
+import inspect
 import logging
 import math
 import os
@@ -27,7 +28,6 @@ from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
     EarlyStoppingCallback,
-    PreTrainedModel,
     Trainer,
     TrainerCallback,
     TrainingArguments,
@@ -48,6 +48,7 @@ from trl import (
 )
 from trl.trainer.utils import RewardDataCollatorWithPadding, pad_to_length
 
+from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils import is_comet_available, is_mlflow_available
@@ -63,7 +64,7 @@ from axolotl.utils.callbacks import (
     log_prediction_callback_factory,
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
@@ -666,7 +667,9 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         return DataLoader(bench_dataset, **dataloader_params)
         # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
 
-    def compute_loss(self, model, inputs, return_outputs=False):
+    def compute_loss(
+        self, model, inputs, return_outputs=False, num_items_in_batch=None
+    ):
         # use one's weighted cross entropy loss calc
         # if self.args.sample_packing:
         #     labels = inputs.pop("labels")
@@ -674,8 +677,18 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         #     loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
         #     return (loss, outputs) if return_outputs else loss
         if self.args.orpo_alpha:
-            return self.orpo_compute_loss(model, inputs, return_outputs=return_outputs)
-        return super().compute_loss(model, inputs, return_outputs=return_outputs)
+            return self.orpo_compute_loss(
+                model,
+                inputs,
+                return_outputs=return_outputs,
+                num_items_in_batch=num_items_in_batch,
+            )
+        return super().compute_loss(
+            model,
+            inputs,
+            return_outputs=return_outputs,
+            num_items_in_batch=num_items_in_batch,
+        )
 
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
@@ -771,7 +784,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         ).squeeze(2)
         return torch.mul(per_token_logps, mask).sum(dim=1) / mask.sum(dim=1)
 
-    def orpo_compute_loss(self, model, inputs, return_outputs=False):
+    def orpo_compute_loss(
+        self,
+        model,
+        inputs,
+        return_outputs=False,
+        num_items_in_batch=None,  # pylint: disable=unused-argument
+    ):
         concat_inputs = AxolotlTrainer.orpo_concatenate_inputs(
             inputs,
             label_pad_token=-100,
@@ -898,6 +917,7 @@ class AxolotlMambaTrainer(AxolotlTrainer):
         model,
         inputs,
         return_outputs=False,  # pylint: disable=unused-argument
+        num_items_in_batch=None,  # pylint: disable=unused-argument
     ):
         input_ids = inputs.pop("input_ids")
         lm_logits = model(input_ids).logits
@@ -1005,18 +1025,32 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         return super().push_to_hub(*args, **kwargs)
 
     def tokenize_row(
-        self, feature, model: Optional[Union[PreTrainedModel, torch.nn.Module]] = None
+        self,
+        features,
+        processing_class,
+        max_prompt_length,
+        max_completion_length,
+        add_special_tokens,
     ) -> Dict:
-        res = super().tokenize_row(feature, model=model)
-        if self.tokenizer.bos_token_id is None and res["prompt_input_ids"][0] is None:
+        res = super().tokenize_row(
+            features,
+            processing_class,
+            max_prompt_length,
+            max_completion_length,
+            add_special_tokens,
+        )
+        if processing_class.bos_token_id is None and res["prompt_input_ids"][0] is None:
             for key in res.keys():
                 res[key] = res[key][1:]
         return res
 
     def training_step(
-        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
+        self,
+        model: nn.Module,
+        inputs: Dict[str, Union[torch.Tensor, Any]],
+        num_items_in_batch=None,
     ) -> torch.Tensor:
-        loss: torch.Tensor = super().training_step(model, inputs)
+        loss: torch.Tensor = super().training_step(model, inputs, num_items_in_batch)
         gc.collect()
         torch.cuda.empty_cache()
         return loss
@@ -1114,17 +1148,28 @@ class TrainerBuilderBase(abc.ABC):
 
     def get_callbacks(self) -> List[TrainerCallback]:
         callbacks = []
+
+        plugin_manager = PluginManager.get_instance()
+        callbacks.extend(
+            plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
+        )
+
         if self.cfg.use_wandb:
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
         if self.cfg.use_mlflow and is_mlflow_available():
+            from transformers.integrations.integration_utils import MLflowCallback
+
             from axolotl.utils.callbacks.mlflow_ import (
                 SaveAxolotlConfigtoMlflowCallback,
             )
 
-            callbacks.append(
-                SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
+            callbacks.extend(
+                [
+                    SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path),
+                    MLflowCallback,
+                ]
             )
         if self.cfg.use_comet and is_comet_available():
             from axolotl.utils.callbacks.comet_ import SaveAxolotlConfigtoCometCallback
@@ -1135,11 +1180,17 @@ class TrainerBuilderBase(abc.ABC):
 
         return callbacks
 
-    @abstractmethod
     def get_post_trainer_create_callbacks(self, trainer):
         """
         Callbacks added after the trainer is created, usually b/c these need access to the trainer
         """
+        callbacks = []
+
+        plugin_manager = PluginManager.get_instance()
+        callbacks.extend(
+            plugin_manager.add_callbacks_post_trainer(cfg=self.cfg, trainer=trainer)
+        )
+        return callbacks
 
     def hook_pre_create_training_args(self, training_arguments_kwargs):
         # TODO
@@ -1185,7 +1236,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
             LogPredictionCallback = log_prediction_callback_factory(
                 trainer, self.tokenizer, "wandb"
@@ -1556,8 +1607,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["model_type"] = self.cfg.model_config_type
         training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
         if self.cfg.chat_template:
-            training_arguments_kwargs["chat_template"] = chat_templates(
-                self.cfg.chat_template
+            training_arguments_kwargs["chat_template"] = get_chat_template(
+                self.cfg.chat_template,
+                tokenizer=self.tokenizer,
             )
 
         if self.cfg.rl == "orpo":
@@ -1662,12 +1714,17 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 return_tensors="pt",
                 **data_collator_kwargs,
             )
+        sig = inspect.signature(trainer_cls)
+        if "processing_class" in sig.parameters.keys():
+            trainer_kwargs["processing_class"] = self.tokenizer
+        else:
+            trainer_kwargs["tokenizer"] = self.tokenizer
+
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,
             eval_dataset=self.eval_dataset,
             args=training_args,
-            tokenizer=self.tokenizer,
             data_collator=self.build_collator(training_args, **data_collator_kwargs),
             callbacks=self.get_callbacks(),
             **trainer_kwargs,
@@ -1708,6 +1765,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         ]
         if self.cfg.reward_model:
             collator = RewardDataCollatorWithPadding
+            if "max_length" in kwargs:
+                kwargs.pop("max_length")
         elif use_batch_sampler_collator:
             if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
                 collator = V2BatchSamplerDataCollatorForSeq2Seq
@@ -1745,7 +1804,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
     def build_training_arguments(self, total_num_steps):
@@ -1831,17 +1890,18 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             # default to saving each epoch if not defined
             training_args_kwargs["save_strategy"] = "epoch"
 
+        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
+
         if self.cfg.rl_beta:
             training_args_kwargs["beta"] = self.cfg.rl_beta
         if self.cfg.orpo_alpha:
             # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
             training_args_kwargs["beta"] = self.cfg.orpo_alpha
 
-        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-        training_args_cls = AxolotlDPOConfig
         if self.cfg.rpo_alpha is not None:
             training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha
 
+        training_args_cls = None
         if self.cfg.rl == "simpo":
             training_args_cls = AxolotlCPOConfig
             training_args_kwargs["loss_type"] = "simpo"
@@ -1850,13 +1910,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-        if self.cfg.rl == "orpo":
+        elif self.cfg.rl == "orpo":
             training_args_cls = AxolotlORPOConfig
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        if self.cfg.rl == "kto":
+        elif self.cfg.rl == "kto":
             training_args_cls = AxolotlKTOConfig
 
             training_args_kwargs["desirable_weight"] = (
@@ -1866,11 +1926,32 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
                 self.cfg.kto_undesirable_weight or 1.0
             )
 
-            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
+        else:
+            training_args_cls = AxolotlDPOConfig
+
+            training_args_kwargs["max_length"] = self.cfg.sequence_len
+
+            training_args_kwargs["max_target_length"] = None
+            if self.cfg.max_prompt_len is not None:
+                training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
+
+            if self.cfg.dpo_use_weighting is not None:
+                training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
+
+            if self.cfg.rl == "ipo":
+                training_args_kwargs["loss_type"] = "ipo"
+                if self.cfg.dpo_label_smoothing:
+                    training_args_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
+            
+            if self.cfg.precompute_ref_log_probs is not None:
+                training_args_kwargs["precompute_ref_log_probs"] = self.cfg.precompute_ref_log_probs
+
+            training_args_kwargs["generate_during_eval"] = self.cfg.use_wandb
+
         training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
             output_dir=self.cfg.output_dir,
             per_device_train_batch_size=self.cfg.micro_batch_size,
@@ -1890,27 +1971,16 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
     def build(self, total_num_steps):
         training_args = self.build_training_arguments(total_num_steps)
         dpo_trainer_kwargs = {}
-        if self.cfg.rl == "ipo":
-            dpo_trainer_kwargs["loss_type"] = "ipo"
-            if self.cfg.dpo_label_smoothing:
-                dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
+        
         if self.eval_dataset:
             dpo_trainer_kwargs["eval_dataset"] = self.eval_dataset
         if self.cfg.adapter and self.peft_config:
             dpo_trainer_kwargs["peft_config"] = self.peft_config
-        if self.cfg.precompute_ref_log_probs is not None:
-            dpo_trainer_kwargs[
-                "precompute_ref_log_probs"
-            ] = self.cfg.precompute_ref_log_probs
+  
         if self.cfg.rl in ["dpo", "ipo"]:
             trainer_cls = AxolotlDPOTrainer
             trainer_cls_args = [self.model, self.model_ref]
 
-            # these aren't used for the ORPO trainer
-            dpo_trainer_kwargs["max_length"] = self.cfg.sequence_len
-            dpo_trainer_kwargs["max_target_length"] = None
-            dpo_trainer_kwargs["max_prompt_length"] = self.cfg.sequence_len
-            dpo_trainer_kwargs["generate_during_eval"] = True
         elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]
@@ -1922,11 +1992,17 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             trainer_cls_args = [self.model]
         else:
             raise ValueError(f"Unsupported RL: {self.cfg.rl}")
+
+        sig = inspect.signature(trainer_cls)
+        if "processing_class" in sig.parameters.keys():
+            dpo_trainer_kwargs["processing_class"] = self.tokenizer
+        else:
+            dpo_trainer_kwargs["tokenizer"] = self.tokenizer
+
         dpo_trainer = trainer_cls(
             *trainer_cls_args,
             args=training_args,
             train_dataset=self.train_dataset,
-            tokenizer=self.tokenizer,
             callbacks=self.get_callbacks(),
             **dpo_trainer_kwargs,
         )
@@ -1948,11 +2024,11 @@ class HFPPOTrainerBuilder(TrainerBuilderBase):
     """
 
     def get_callbacks(self):
-        callbacks = []
+        callbacks = super().get_callbacks()
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
     def build(self, total_num_steps):

src/axolotl/integrations/base.py

@@ -18,9 +18,10 @@ Plugins can be used to integrate third-party models, modify the training process
 
 To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.
 """
+import collections
 import importlib
 import logging
-from typing import List
+from typing import OrderedDict
 
 
 class BasePlugin:
@@ -47,7 +48,7 @@ class BasePlugin:
         Initializes the BasePlugin.
         """
 
-    def register(self, cfg):
+    def register(self, cfg):  # pylint: disable=unused-argument
         """
         Registers the plugin with the given configuration.
 
@@ -63,7 +64,7 @@ class BasePlugin:
         Returns a pydantic model for the plugin's input arguments.
         """
 
-    def pre_model_load(self, cfg):
+    def pre_model_load(self, cfg):  # pylint: disable=unused-argument
         """
         Performs actions before the model is loaded.
 
@@ -74,7 +75,7 @@ class BasePlugin:
         None
         """
 
-    def post_model_load(self, cfg, model):
+    def post_model_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after the model is loaded.
 
@@ -86,7 +87,7 @@ class BasePlugin:
         None
         """
 
-    def pre_lora_load(self, cfg, model):
+    def pre_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions before LoRA weights are loaded.
 
@@ -98,7 +99,7 @@ class BasePlugin:
         None
         """
 
-    def post_lora_load(self, cfg, model):
+    def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after LoRA weights are loaded.
 
@@ -110,7 +111,7 @@ class BasePlugin:
         None
         """
 
-    def create_optimizer(self, cfg, trainer):
+    def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
         """
         Creates and returns an optimizer for training.
 
@@ -122,7 +123,9 @@ class BasePlugin:
         object: The created optimizer.
         """
 
-    def create_lr_scheduler(self, cfg, trainer, optimizer):
+    def create_lr_scheduler(
+        self, cfg, trainer, optimizer
+    ):  # pylint: disable=unused-argument
         """
         Creates and returns a learning rate scheduler.
 
@@ -135,7 +138,7 @@ class BasePlugin:
         object: The created learning rate scheduler.
         """
 
-    def add_callbacks_pre_trainer(self, cfg, model):
+    def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
         """
         Adds callbacks to the trainer before training.
 
@@ -146,8 +149,11 @@ class BasePlugin:
         Returns:
         List[callable]: A list of callback functions to be added to the TrainingArgs
         """
+        return []
 
-    def add_callbacks_post_trainer(self, cfg, trainer):
+    def add_callbacks_post_trainer(
+        self, cfg, trainer
+    ):  # pylint: disable=unused-argument
         """
         Adds callbacks to the trainer after training.
 
@@ -158,8 +164,9 @@ class BasePlugin:
         Returns:
         List[callable]: A list of callback functions to be added to the TrainingArgs
         """
+        return []
 
-    def post_train(self, cfg, model):
+    def post_train(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after training is complete.
 
@@ -171,7 +178,7 @@ class BasePlugin:
         None
         """
 
-    def post_train_unload(self, cfg):
+    def post_train_unload(self, cfg):  # pylint: disable=unused-argument
         """
         Performs actions after training is complete and the model is unloaded.
 
@@ -227,7 +234,7 @@ class PluginManager:
     pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
     """
 
-    plugins: List[BasePlugin] = []
+    plugins: OrderedDict[str, BasePlugin] = collections.OrderedDict()
 
     _instance = None
 
@@ -237,7 +244,7 @@ class PluginManager:
         """
         if cls._instance is None:
             cls._instance = super(PluginManager, cls).__new__(cls)
-            cls._instance.plugins: List[BasePlugin] = []
+            cls._instance.plugins = collections.OrderedDict()
         return cls._instance
 
     @staticmethod
@@ -265,7 +272,7 @@ class PluginManager:
         """
         try:
             plugin = load_plugin(plugin_name)
-            self.plugins.append(plugin)
+            self.plugins[plugin_name] = plugin
         except ImportError:
             logging.error(f"Failed to load plugin: {plugin_name}")
 
@@ -277,7 +284,7 @@ class PluginManager:
         list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
         """
         input_args = []
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             input_args_from_plugin = plugin.get_input_args()
             if input_args_from_plugin is not None:
                 input_args.append(input_args_from_plugin)
@@ -293,7 +300,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.pre_model_load(cfg)
 
     def post_model_load(self, cfg, model):
@@ -307,7 +314,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_model_load(cfg, model)
 
     def pre_lora_load(self, cfg, model):
@@ -321,7 +328,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.pre_lora_load(cfg, model)
 
     def post_lora_load(self, cfg, model):
@@ -335,7 +342,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_lora_load(cfg, model)
 
     def create_optimizer(self, cfg, trainer):
@@ -349,7 +356,7 @@ class PluginManager:
         Returns:
         object: The created optimizer, or None if none was found.
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             optimizer = plugin.create_optimizer(cfg, trainer)
             if optimizer is not None:
                 return optimizer
@@ -367,7 +374,7 @@ class PluginManager:
         Returns:
         object: The created learning rate scheduler, or None if none was found.
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
             if scheduler is not None:
                 return scheduler
@@ -385,7 +392,7 @@ class PluginManager:
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             callbacks.extend(plugin.add_callbacks_pre_trainer(cfg, model))
         return callbacks
 
@@ -401,7 +408,7 @@ class PluginManager:
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
         return callbacks
 
@@ -416,5 +423,5 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_train_unload(cfg)

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -22,7 +22,6 @@ from transformers.models.llama.modeling_llama import (
     apply_rotary_pos_emb,
     repeat_kv,
 )
-from xformers.ops import SwiGLU
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids, set_module_name
 
@@ -44,7 +43,19 @@ except ImportError:
 LOG = logging.getLogger("axolotl")
 
 
+def is_xformers_available() -> bool:
+    try:
+        import xformers  # pylint: disable=unused-import  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
 def is_xformers_swiglu_available() -> bool:
+    if not is_xformers_available():
+        return False
+
     from xformers.ops.common import get_xformers_operator
 
     try:
@@ -57,6 +68,11 @@ def is_xformers_swiglu_available() -> bool:
 
 
 def replace_llama_mlp_with_swiglu(model):
+    if is_xformers_swiglu_available():
+        from axolotl.monkeypatch.xformers_ import FusedMLP
+    else:
+        raise RuntimeError("xformers SwiGLU not available for this environment")
+
     for name, module in model.named_modules():
         if isinstance(module, LlamaMLP):
             mlp = FusedMLP(
@@ -181,49 +197,6 @@ class FusedAttention(LlamaAttention):
         set_module_name(model, name, new_attn)
 
 
-class FusedMLP(torch.nn.Module):
-    """
-    Fused MLP layer for incrementally improved training efficiency
-    """
-
-    def __init__(
-        self,
-        config,
-        gate_proj: torch.nn.Linear,
-        up_proj: torch.nn.Linear,
-        down_proj: torch.nn.Linear,
-    ):
-        super().__init__()
-        self.config = config
-        self.swiglu = SwiGLU(
-            in_features=config.hidden_size,
-            hidden_features=config.intermediate_size,
-            bias=False,
-            _pack_weights=True,
-        )
-        # overwrite initialized weights with pretrained weights
-        self.swiglu.w12.weight.data = torch.cat(
-            (gate_proj.weight.data, up_proj.weight.data), dim=0
-        )
-        self.swiglu.w3.weight.data = down_proj.weight.data
-
-    def _post_training(self, model, name):
-        w1, w2 = torch.split(  # pylint: disable=invalid-name
-            self.swiglu.w12.weight.data, self.config.intermediate_size, dim=0
-        )
-
-        # Assign the split weights back to the original layers
-        new_mlp = LlamaMLP(self.config)
-        new_mlp.gate_proj.weight.data = w1
-        new_mlp.up_proj.weight.data = w2
-        new_mlp.down_proj.weight.data = self.swiglu.w3.weight.data
-
-        set_module_name(model, name, new_mlp)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:  # pylint: disable=invalid-name
-        return self.swiglu(x)
-
-
 # Disable the transformation of the attention mask in LlamaModel as the flash attention
 # requires the attention mask to be the same as the key_padding_mask
 def _prepare_decoder_attention_mask(

src/axolotl/monkeypatch/unsloth_.py

@@ -16,26 +16,6 @@ from transformers.models.llama.modeling_llama import (
 
 LOG = get_logger("axolotl.monkeypatch.unsloth")
 
-ORIGINAL_CEL_CODE = """# 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)
-"""
-
-PATCHED_CEL_CODE = """shift_logits = logits[..., :-1, :].contiguous()
-        shift_labels = labels[..., 1:].contiguous()
-        loss = fast_cross_entropy_loss(
-            logits = shift_logits,
-            labels = shift_labels,
-        )
-"""
-
 ORIGINAL_QKV_CODE = """
     query_states = self.q_proj(hidden_states)
     key_states = self.k_proj(hidden_states)
@@ -80,12 +60,6 @@ def get_forward_code() -> str:
     return forward
 
 
-def check_cel_is_patchable() -> bool:
-    forward = get_forward_code()
-    forward, _ = detab_code(forward)
-    return ORIGINAL_CEL_CODE in forward
-
-
 def get_self_attn_code() -> str:
     forward = inspect.getsource(LlamaFlashAttention2.forward)
     return forward
@@ -98,48 +72,31 @@ def check_self_attn_is_patchable() -> bool:
 
 
 def integrate_cross_entropy_loss_patch(model_type: str = "llama") -> None:
+    from unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss
+
+    def UnslothForCausalLMLoss(  # pylint: disable=invalid-name
+        logits,
+        labels,
+        vocab_size: int,  # pylint: disable=unused-argument
+        num_items_in_batch: int = None,
+        ignore_index: int = -100,  # pylint: disable=unused-argument
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        # 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()
+
+        loss = fast_cross_entropy_loss(
+            logits=shift_logits, labels=shift_labels, n_items=num_items_in_batch
+        )
+        return loss
+
     if model_type == "llama":
-        forward = get_forward_code()
-        LlamaForCausalLM._original_forward = forward  # pylint: disable=protected-access
-        forward, _ = detab_code(forward)
-        assert ORIGINAL_CEL_CODE in forward, "Original forward code not found"
+        from transformers.loss import loss_utils
 
-        forward = forward.replace(
-            "@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)", ""
-        )
-        forward = forward.replace(
-            "@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)",
-            "",
-        )
-        forward = forward.replace(ORIGINAL_CEL_CODE, PATCHED_CEL_CODE)
-        forward = forward.replace(
-            "def forward(",
-            "def fast_cross_entropy_loss_forward(",
-            1,
-        )
-
-        # load imports necessary
-        import transformers.models.llama.modeling_llama
-
-        items_to_import = []
-        for item in dir(transformers.models.llama.modeling_llama):
-            if item in forward:
-                items_to_import.append(item)
-
-        exec(  # pylint: disable=exec-used  # nosec B102
-            "from unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss",
-            globals(),
-        )
-
-        exec(  # pylint: disable=exec-used  # nosec B102
-            "from transformers.models.llama.modeling_llama import ("
-            + ", ".join(x for x in items_to_import)
-            + ")",
-            globals(),
-        )
-        exec(forward, globals())  # pylint: disable=exec-used  # nosec B102
-        LOG.info("patching unsloth fast_cross_entropy_loss", main_process_only=True)
-        LlamaForCausalLM.forward = fast_cross_entropy_loss_forward  # pylint: disable=undefined-variable  # noqa: F821
+        loss_utils.ForCausalLMLoss = UnslothForCausalLMLoss  # type: ignore[assignment]
     else:
         raise ValueError("Unsupported model type")
 

src/axolotl/monkeypatch/xformers_/__init__.py

@@ -0,0 +1,51 @@
+"""
+Fused MLP layer for incrementally improved training efficiency
+"""
+import torch
+from transformers.models.llama.modeling_llama import LlamaMLP
+from xformers.ops import SwiGLU
+
+from axolotl.monkeypatch.utils import set_module_name
+
+
+class FusedMLP(torch.nn.Module):
+    """
+    Fused MLP layer for incrementally improved training efficiency
+    """
+
+    def __init__(
+        self,
+        config,
+        gate_proj: torch.nn.Linear,
+        up_proj: torch.nn.Linear,
+        down_proj: torch.nn.Linear,
+    ):
+        super().__init__()
+        self.config = config
+        self.swiglu = SwiGLU(
+            in_features=config.hidden_size,
+            hidden_features=config.intermediate_size,
+            bias=False,
+            _pack_weights=True,
+        )
+        # overwrite initialized weights with pretrained weights
+        self.swiglu.w12.weight.data = torch.cat(
+            (gate_proj.weight.data, up_proj.weight.data), dim=0
+        )
+        self.swiglu.w3.weight.data = down_proj.weight.data
+
+    def _post_training(self, model, name):
+        w1, w2 = torch.split(  # pylint: disable=invalid-name
+            self.swiglu.w12.weight.data, self.config.intermediate_size, dim=0
+        )
+
+        # Assign the split weights back to the original layers
+        new_mlp = LlamaMLP(self.config)
+        new_mlp.gate_proj.weight.data = w1
+        new_mlp.up_proj.weight.data = w2
+        new_mlp.down_proj.weight.data = self.swiglu.w3.weight.data
+
+        set_module_name(model, name, new_mlp)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:  # pylint: disable=invalid-name
+        return self.swiglu(x)

src/axolotl/prompt_strategies/bradley_terry/__init__.py

@@ -6,7 +6,7 @@ import logging
 
 from axolotl.prompt_strategies.user_defined import UserDefinedDatasetConfig
 
-LOG = logging.getLogger("axolotl.prompt_strategies")
+LOG = logging.getLogger("axolotl.prompt_strategies.bradley_terry")
 
 
 def load(strategy, tokenizer, cfg, ds_cfg):

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -2,13 +2,18 @@
 Bradley-Terry model with chat template prompt strategy.
 """
 
+import logging
 from typing import Any, Dict, Optional
 
 from axolotl.prompt_strategies.chat_template import (
     ChatTemplatePrompter,
     ChatTemplateStrategy,
 )
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template_from_config
+
+# Configure the logger
+LOG = logging.getLogger("axolotl.prompt_strategies.bradley_terry.chat_template")
+LOG.setLevel(logging.INFO)
 
 
 class BTChatTemplateStrategy(ChatTemplateStrategy):
@@ -27,18 +32,24 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
         # pylint: disable=duplicate-code
         prompt[self.messages] = []
         if prompt["system"]:
-            prompt[self.messages].append({"from": "system", "value": prompt["system"]})
-        prompt[self.messages].append({"from": "user", "value": prompt["input"]})
-        prompt[self.messages].append({"from": "assistant", "value": prompt["chosen"]})
+            prompt[self.messages].append(
+                {"role": "system", "content": prompt["system"]}
+            )
+        prompt[self.messages].append({"role": "user", "content": prompt["input"]})
+        prompt[self.messages].append({"role": "assistant", "content": prompt["chosen"]})
         chosen_tokenized = super().tokenize_prompt(prompt)
 
         self.messages = "rejected_messages"
         # pylint: disable=duplicate-code
         prompt[self.messages] = []
         if prompt["system"]:
-            prompt[self.messages].append({"from": "system", "value": prompt["system"]})
-        prompt[self.messages].append({"from": "user", "value": prompt["input"]})
-        prompt[self.messages].append({"from": "assistant", "value": prompt["rejected"]})
+            prompt[self.messages].append(
+                {"role": "system", "content": prompt["system"]}
+            )
+        prompt[self.messages].append({"role": "user", "content": prompt["input"]})
+        prompt[self.messages].append(
+            {"role": "assistant", "content": prompt["rejected"]}
+        )
         rejected_tokenized = super().tokenize_prompt(prompt)
 
         return {
@@ -53,15 +64,18 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
 
 def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     ds_cfg = ds_cfg or {}
+    chat_template_string = get_chat_template_from_config(
+        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
+    )
 
     prompter_params = {
         "tokenizer": tokenizer,
-        "chat_template": chat_templates(ds_cfg.get("chat_template", "chatml")),
-        "message_field_role": ds_cfg.get("message_field_role", "from"),
-        "message_field_content": ds_cfg.get("message_field_content", "value"),
-        "message_field_training": ds_cfg.get("message_field_training", "training"),
+        "chat_template": chat_template_string,
+        "message_field_role": ds_cfg.get("message_field_role", "role"),
+        "message_field_content": ds_cfg.get("message_field_content", "content"),
+        "message_field_training": ds_cfg.get("message_field_training", None),
         "message_field_training_detail": ds_cfg.get(
-            "message_field_training_detail", "train_detail"
+            "message_field_training_detail", None
         ),
         "roles": ds_cfg.get("roles"),
         "drop_system_message": ds_cfg.get("drop_system_message", False),
@@ -74,8 +88,8 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     strategy_params = {
         "train_on_inputs": cfg.train_on_inputs,
         "sequence_len": cfg.sequence_len,
-        "roles_to_train": ds_cfg.get("roles_to_train", ["gpt", "assistant"]),
-        "train_on_eos": ds_cfg.get("train_on_eos", "turn"),
+        "roles_to_train": ds_cfg.get("roles_to_train", []),
+        "train_on_eos": ds_cfg.get("train_on_eos", None),
     }
 
     strategy = BTChatTemplateStrategy(

src/axolotl/prompt_strategies/chat_template.py

@@ -9,7 +9,7 @@ from transformers import ProcessorMixin
 
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template_from_config
 
 # Configure the logger
 LOG = logging.getLogger("axolotl")
@@ -405,10 +405,14 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None, processor=None):
     # pylint: disable=duplicate-code
     ds_cfg = ds_cfg or {}
+    chat_template_string = get_chat_template_from_config(
+        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
+    )
+    LOG.info(f"Using chat template:\n---\n{chat_template_string!s}\n---")
 
     prompter_params = {
         "tokenizer": tokenizer,
-        "chat_template": chat_templates(ds_cfg.get("chat_template", "chatml")),
+        "chat_template": chat_template_string,
         "message_field_role": ds_cfg.get("message_field_role", "role"),
         "message_field_content": ds_cfg.get("message_field_content", "content"),
         "message_field_training": ds_cfg.get("message_field_training", None),

src/axolotl/prompt_strategies/dpo/chat_template.py

@@ -2,15 +2,16 @@
 DPO prompt strategies for using tokenizer chat templates.
 """
 
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import extract_chat_template_args, get_chat_template
 
 
 def default(
     cfg, dataset_idx=0, **kwargs
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     ds_cfg = cfg["datasets"][dataset_idx]
-    chat_template_str = chat_templates(cfg.chat_template)
-
+    chat_template_choice, chat_template_jinja = extract_chat_template_args(
+        cfg=cfg, ds_cfg=ds_cfg
+    )
     field_messages = ds_cfg.get("field_messages", "messages")
     field_chosen = ds_cfg.get("field_chosen", "chosen")
     field_rejected = ds_cfg.get("field_rejected", "rejected")
@@ -30,6 +31,12 @@ def default(
             role_map[source] = target
 
     def transform_fn(sample, tokenizer=None):
+        chat_template_string = get_chat_template(
+            user_choice=chat_template_choice,
+            jinja_template=chat_template_jinja,
+            tokenizer=tokenizer,
+        )
+
         messages = sample[field_messages]
         messages = [
             {
@@ -46,28 +53,29 @@ def default(
             "role": role_map[sample[field_rejected][field_message_role]],
             "content": sample[field_rejected][field_message_content],
         }
+        dummy_user_message = {"role": "user", "content": "[[dummy_message]]"}
 
         result = {}
         result["prompt"] = tokenizer.apply_chat_template(
             messages,
             add_generation_prompt=True,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
 
         result["chosen"] = tokenizer.apply_chat_template(
-            [chosen],
+            [dummy_user_message, chosen],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
         chosen_strip_index = result["chosen"].find(chosen["content"])
         result["chosen"] = result["chosen"][chosen_strip_index:].rstrip()
 
         result["rejected"] = tokenizer.apply_chat_template(
-            [rejected],
+            [dummy_user_message, rejected],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
         rejected_strip_index = result["rejected"].find(rejected["content"])

src/axolotl/prompt_strategies/orpo/chat_template.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel
 
 from axolotl.prompt_tokenizers import IGNORE_INDEX, PromptTokenizingStrategy
 from axolotl.prompters import Prompter
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template_from_config
 
 
 class Message(BaseModel):
@@ -28,18 +28,13 @@ def load(
     """
     chatml transforms for datasets with system, input, chosen, rejected
     """
-
-    chat_template = chat_templates("chatml")
-    if ds_cfg and "chat_template" in ds_cfg:
-        chat_template = ds_cfg["chat_template"]
-        try:
-            chat_template = chat_templates(chat_template)
-        except ValueError:
-            pass
-    tokenizer.chat_template = chat_template
+    chat_template_string = get_chat_template_from_config(
+        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
+    )
+    tokenizer.chat_template = chat_template_string
 
     return ORPOTokenizingStrategy(
-        ORPOPrompter(chat_template, tokenizer),
+        ORPOPrompter(chat_template_string, tokenizer),
         tokenizer,
         cfg.train_on_inputs,
         cfg.sequence_len,
@@ -248,28 +243,30 @@ class ORPOPrompter(Prompter):
 def argilla(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-argument
     dataset_parser = ORPODatasetParsingStrategy()
 
-    chat_template_str = chat_templates(cfg.chat_template)
-
     def transform_fn(sample, tokenizer=None):
         res = {}
 
+        chat_template_string = get_chat_template_from_config(
+            cfg=cfg, tokenizer=tokenizer
+        )
+
         res["prompt"] = tokenizer.apply_chat_template(
             [msg.model_dump() for msg in dataset_parser.get_prompt(sample).messages],
             add_generation_prompt=True,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
         prompt_str_len = len(res["prompt"])
         res["chosen"] = tokenizer.apply_chat_template(
             [msg.model_dump() for msg in dataset_parser.get_chosen(sample).messages],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )[prompt_str_len:]
         res["rejected"] = tokenizer.apply_chat_template(
             [msg.model_dump() for msg in dataset_parser.get_rejected(sample).messages],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )[prompt_str_len:]
 

src/axolotl/prompt_strategies/sharegpt.py

@@ -62,7 +62,7 @@ def build_loader(
 ):
     def _load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
         LOG.warning(
-            "sharegpt type support will be deprecated in the next release of Axolotl. Please use chat_template instead.",
+            "sharegpt type support will be deprecated in the next release of Axolotl. Please use chat_template instead. https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template",
         )
         conversation = (
             ds_cfg["conversation"]

src/axolotl/train.py

@@ -260,8 +260,10 @@ def train(
 
     if not cfg.hub_model_id:
         try:
-            trainer.create_model_card(model_name=cfg.output_dir.lstrip("./"))
-        except AttributeError:
+            trainer.create_model_card(
+                model_name=cfg.output_dir.lstrip("./").encode("utf-8").decode("utf-8")
+            )
+        except (AttributeError, UnicodeDecodeError):
             pass
     elif cfg.hub_model_id:
         # defensively push to the hub to ensure the model card is updated

src/axolotl/utils/config/__init__.py

@@ -228,6 +228,7 @@ def normalize_cfg_datasets(cfg):
                         f"updating dataset {ds_cfg.path} with `chat_template: {cfg.chat_template}` to match your chat_template"
                     )
                     cfg.datasets[idx].chat_template = cfg.chat_template
+                    cfg.datasets[idx].chat_template_jinja = cfg.chat_template_jinja
 
 
 def validate_config(cfg: DictDefault, capabilities: Optional[dict] = None):

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -8,9 +8,16 @@ import logging
 import os
 from enum import Enum
 from importlib.metadata import version
-from typing import Any, Dict, List, Literal, Optional, Tuple, Union
+from typing import Annotated, Any, Dict, List, Literal, Optional, Tuple, Union
 
-from pydantic import BaseModel, Field, conlist, field_validator, model_validator
+from pydantic import (
+    BaseModel,
+    Field,
+    StringConstraints,
+    conlist,
+    field_validator,
+    model_validator,
+)
 from transformers import SchedulerType
 from transformers.training_args import OptimizerNames
 
@@ -21,6 +28,38 @@ LOG = logging.getLogger("axolotl.utils.config.models.input")
 SUPPORTED_METRICS = {"sacrebleu", "comet", "ter", "chrf", "perplexity"}
 
 
+class RLType(str, Enum):
+    """RL trainer type configuration subset"""
+
+    dpo = "dpo"  # pylint: disable=invalid-name
+    ipo = "ipo"  # pylint: disable=invalid-name
+    orpo = "orpo"  # pylint: disable=invalid-name
+    kto = "kto"  # pylint: disable=invalid-name
+    simpo = "simpo"  # pylint: disable=invalid-name
+
+
+class ChatTemplate(str, Enum):
+    """Chat templates configuration subset"""
+
+    alpaca = "alpaca"  # pylint: disable=invalid-name
+    chatml = "chatml"  # pylint: disable=invalid-name
+    mistral_v1 = "mistral_v1"  # pylint: disable=invalid-name
+    mistral_v2v3 = "mistral_v2v3"  # pylint: disable=invalid-name
+    mistral_v3_tekken = "mistral_v3_tekken"  # pylint: disable=invalid-name
+    gemma = "gemma"  # pylint: disable=invalid-name
+    cohere = "cohere"  # pylint: disable=invalid-name
+    llama3 = "llama3"  # pylint: disable=invalid-name
+    llama3_2_vision = "llama3_2_vision"  # pylint: disable=invalid-name
+    phi_3 = "phi_3"  # pylint: disable=invalid-name
+    phi_35 = "phi_35"  # pylint: disable=invalid-name
+    deepseek_v2 = "deepseek_v2"  # pylint: disable=invalid-name
+    jamba = "jamba"  # pylint: disable=invalid-name
+    jinja = "jinja"  # pylint: disable=invalid-name
+    qwen_25 = "qwen_25"  # pylint: disable=invalid-name
+    tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
+    exaone = "exaone"  # pylint: disable=invalid-name
+
+
 class DeprecatedParameters(BaseModel):
     """configurations that are deprecated"""
 
@@ -105,13 +144,19 @@ class SFTDataset(BaseModel):
     input_transform: Optional[str] = None
     shards: Optional[int] = None
     conversation: Optional[str] = None
-    chat_template: Optional[str] = None
+    # Do not make this too strict or it will break the validator to choose different dataset class
+    chat_template: Optional[
+        Union[
+            ChatTemplate,
+            str,
+        ]
+    ] = None
+    chat_template_jinja: Optional[str] = None
     data_files: Optional[Union[str, List[str]]] = None
     input_format: Optional[str] = None
     name: Optional[str] = None
     ds_type: Optional[str] = None
     train_on_split: Optional[str] = None
-
     field: Optional[str] = None
     field_human: Optional[str] = None
     field_model: Optional[str] = None
@@ -122,13 +167,32 @@ class SFTDataset(BaseModel):
     message_field_training_detail: Optional[str] = None
     roles_to_train: Optional[List[str]] = None
     train_on_eos: Optional[str] = None
-
     roles: Optional[Dict[str, List[str]]] = None
     drop_system_message: Optional[bool] = None
-
     trust_remote_code: Optional[bool] = False
     revision: Optional[str] = None
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_chat_template_config(cls, data):
+        # Set chat_template to tokenizer_default if not set
+        if data.get("type") == "chat_template" and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.tokenizer_default
+
+        # if chat_template is set to jinja, chat_template_jinja is required
+        if data.get("chat_template") == ChatTemplate.jinja and not data.get(
+            "chat_template_jinja"
+        ):
+            raise ValueError(
+                "chat_template_jinja is required when chat_template is set to jinja"
+            )
+
+        # If chat_template_jinja is set, set chat_template to jinja
+        if data.get("chat_template_jinja") and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.jinja
+
+        return data
+
 
 class UserDefinedDPOType(BaseModel):
     """User defined typing for DPO"""
@@ -174,35 +238,6 @@ class KTODataset(BaseModel):
     revision: Optional[str] = None
 
 
-class RLType(str, Enum):
-    """RL trainer type configuration subset"""
-
-    dpo = "dpo"  # pylint: disable=invalid-name
-    ipo = "ipo"  # pylint: disable=invalid-name
-    orpo = "orpo"  # pylint: disable=invalid-name
-    kto = "kto"  # pylint: disable=invalid-name
-    simpo = "simpo"  # pylint: disable=invalid-name
-
-
-class ChatTemplate(str, Enum):
-    """Chat templates configuration subset"""
-
-    alpaca = "alpaca"  # pylint: disable=invalid-name
-    chatml = "chatml"  # pylint: disable=invalid-name
-    mistral_v1 = "mistral_v1"  # pylint: disable=invalid-name
-    mistral_v2v3 = "mistral_v2v3"  # pylint: disable=invalid-name
-    mistral_v3_tekken = "mistral_v3_tekken"  # pylint: disable=invalid-name
-    gemma = "gemma"  # pylint: disable=invalid-name
-    cohere = "cohere"  # pylint: disable=invalid-name
-    llama3 = "llama3"  # pylint: disable=invalid-name
-    llama3_2_vision = "llama3_2_vision"  # pylint: disable=invalid-name
-    phi_3 = "phi_3"  # pylint: disable=invalid-name
-    phi_35 = "phi_35"  # pylint: disable=invalid-name
-    deepseek_v2 = "deepseek_v2"  # pylint: disable=invalid-name
-    jamba = "jamba"  # pylint: disable=invalid-name
-    qwen_25 = "qwen_25"  # pylint: disable=invalid-name
-
-
 class LoftQConfig(BaseModel):
     """LoftQ configuration subset"""
 
@@ -549,9 +584,13 @@ class AxolotlInputConfig(
     resume_from_checkpoint: Optional[str] = None
     auto_resume_from_checkpoints: Optional[bool] = None
     resize_token_embeddings_to_32x: Optional[bool] = None
+    mean_resizing_embeddings: Optional[bool] = False
 
     rl: Optional[RLType] = None
     reward_model: Optional[bool] = None
+    dpo_use_weighting: Optional[
+        bool
+    ] = None  # whether to use weighting in DPO trainer. If none, default is false in the trainer.
 
     datasets: Optional[conlist(Union[SFTDataset, DPODataset, KTODataset], min_length=1)] = None  # type: ignore
     test_datasets: Optional[conlist(Union[SFTDataset, DPODataset, KTODataset], min_length=1)] = None  # type: ignore
@@ -718,7 +757,13 @@ class AxolotlInputConfig(
     gpu_memory_limit: Optional[Union[int, str]] = None
     low_cpu_mem_usage: Optional[bool] = None
 
-    chat_template: Optional[ChatTemplate] = None
+    chat_template: Optional[
+        Union[
+            ChatTemplate,
+            Annotated[str, StringConstraints(pattern="^tokenizer_default_fallback_")],
+        ]
+    ] = None
+    chat_template_jinja: Optional[str] = None
     default_system_message: Optional[str] = None
 
     fix_untrained_tokens: Optional[bool] = None
@@ -827,6 +872,23 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_chat_template_config(cls, data):
+        # if chat_template is set to jinja, chat_template_jinja is required
+        if data.get("chat_template") == ChatTemplate.jinja and not data.get(
+            "chat_template_jinja"
+        ):
+            raise ValueError(
+                "chat_template_jinja is required when chat_template is set to jinja"
+            )
+
+        # If chat_template_jinja is set, set chat_template to jinja
+        if data.get("chat_template_jinja") and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.jinja
+
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_sample_packing_wo_flash(cls, data):

src/axolotl/utils/data/sft.py

@@ -2,9 +2,11 @@
 
 import functools
 import logging
+import time
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
+import requests
 from datasets import (
     Dataset,
     DatasetDict,
@@ -53,6 +55,28 @@ from axolotl.utils.trainer import (
 LOG = logging.getLogger("axolotl")
 
 
+def retry_on_request_exceptions(max_retries=3, delay=1):
+    def decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):  # pylint: disable=inconsistent-return-statements
+            for attempt in range(max_retries):
+                try:
+                    return func(*args, **kwargs)
+                except (
+                    requests.exceptions.ReadTimeout,
+                    requests.exceptions.ConnectionError,
+                ) as exc:
+                    if attempt < max_retries - 1:
+                        time.sleep(delay)
+                    else:
+                        raise exc
+
+        return wrapper
+
+    return decorator
+
+
+@retry_on_request_exceptions(max_retries=3, delay=5)
 def prepare_dataset(cfg, tokenizer, processor=None):
     prompters = []
     if not cfg.pretraining_dataset:

src/axolotl/utils/mlflow_.py

@@ -16,3 +16,7 @@ def setup_mlflow_env_vars(cfg: DictDefault):
     # Enable mlflow if experiment name is present
     if cfg.mlflow_experiment_name and len(cfg.mlflow_experiment_name) > 0:
         cfg.use_mlflow = True
+
+    # Enable logging hf artifacts in mlflow if value is truthy
+    if cfg.hf_mlflow_log_artifacts is True:
+        os.environ["HF_MLFLOW_LOG_ARTIFACTS"] = "true"

src/axolotl/utils/samplers/multipack.py

@@ -133,6 +133,8 @@ class MultipackBatchSampler(BatchSampler):
         self.eff_total_used = 0
         self.eff_total_slots = 0
 
+        self.len_across_ranks = None
+
     def set_epoch(self, epoch: int):
         self.epoch = epoch
 
@@ -195,15 +197,14 @@ class MultipackBatchSampler(BatchSampler):
             LOG.info(f"gather_len_batches: {repr(estimates)}")
             return math.floor(0.998 * min(estimates))
 
-        min_len_batches = reduce_and_broadcast(
-            lambda: num,
-            calc_min_len,
-        )
+        min_len_batches = reduce_and_broadcast(lambda: num, calc_min_len)
         return min_len_batches
 
     def __len__(self):
-        len_batches = self.num_batches()
-        return self.gather_len_batches(len_batches)
+        if not self.len_across_ranks:
+            len_batches = self.num_batches()
+            self.len_across_ranks = self.gather_len_batches(len_batches)
+        return self.len_across_ranks
 
     def _len_est(self):
         efficiency = (

test.yml

@@ -0,0 +1,59 @@
+base_model: JackFram/llama-68m
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: arcee-ai/distilabel-intel-orca-dpo-pairs-binarized
+    type: chatml.ultra
+    split: train
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/out
+
+sequence_len: 2048
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+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
+
+rl: dpo
+dpo_use_weighting: true
+
+warmup_steps: 10
+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|>

test2.yml

@@ -0,0 +1,43 @@
+base_model: JackFram/llama-68m
+
+load_in_8bit: true
+
+datasets:
+  - path: arcee-ai/distilabel-intel-orca-dpo-pairs-binarized
+    type: chatml.ultra
+    split: train
+output_dir: ./outputs/lora-out
+
+sequence_len: 1024
+
+adapter: lora
+lora_r: 64
+lora_alpha: 32
+lora_dropout: 0.1
+lora_target_linear: true
+
+rl: dpo
+dpo_use_weighting: true
+
+wandb_project: check_dpotrainer
+wandb_entity: axolotl-ai
+wandb_watch:
+wandb_name: baseline/dpo_base/dpo_use_weighting
+wandb_log_model:
+
+
+num_epochs: 1
+micro_batch_size: 4
+gradient_accumulation_steps: 1
+learning_rate: 0.00001
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+max_steps": 20
+save_steps: 10
+warmup_steps: 5
+gradient_checkpointing: True
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+#special_tokens:
+#  pad_token: <|end_of_text|>
+

tests/e2e/multigpu/test_eval.py

@@ -0,0 +1,155 @@
+"""
+E2E tests for multigpu eval
+"""
+import logging
+import os
+import unittest
+from pathlib import Path
+
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+
+from axolotl.utils.dict import DictDefault
+
+from ..utils import with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
+os.environ["WANDB_DISABLED"] = "true"
+
+AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
+
+
+class TestMultiGPUEval(unittest.TestCase):
+    """
+    Test case for MultiGPU Eval Sample Packing
+    """
+
+    @with_temp_dir
+    def test_eval_sample_packing(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "load_in_8bit": False,
+                "load_in_4bit": True,
+                "strict": False,
+                "sequence_len": 2048,
+                "adapter": "qlora",
+                "sample_packing": True,
+                "eval_sample_packing": True,
+                "pad_to_sequence_len": True,
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "lora_modules_to_save": ["embed_tokens", "lm_head"],
+                "val_set_size": 0.1,
+                "special_tokens": {"pad_token": "<|end_of_text|>"},
+                "datasets": [
+                    {
+                        "path": "teknium/GPT4-LLM-Cleaned",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "loss_watchdog_threshold": 5.0,
+                "loss_watchdog_patience": 3,
+                "bf16": "auto",
+                "warmup_steps": 1,
+                "evals_per_epoch": 2,
+                "eval_max_new_tokens": 128,
+                "saves_per_epoch": 1,
+                "logging_steps": 1,
+                "weight_decay": 0.0,
+            }
+        )
+
+        # 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",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )
+
+    @with_temp_dir
+    def test_eval(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "load_in_8bit": False,
+                "load_in_4bit": True,
+                "strict": False,
+                "sequence_len": 2048,
+                "adapter": "qlora",
+                "sample_packing": True,
+                "eval_sample_packing": False,
+                "pad_to_sequence_len": True,
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "lora_modules_to_save": ["embed_tokens", "lm_head"],
+                "val_set_size": 0.1,
+                "special_tokens": {"pad_token": "<|end_of_text|>"},
+                "datasets": [
+                    {
+                        "path": "teknium/GPT4-LLM-Cleaned",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "loss_watchdog_threshold": 5.0,
+                "loss_watchdog_patience": 3,
+                "bf16": "auto",
+                "warmup_steps": 1,
+                "evals_per_epoch": 2,
+                "eval_max_new_tokens": 128,
+                "saves_per_epoch": 1,
+                "logging_steps": 1,
+                "weight_decay": 0.0,
+            }
+        )
+
+        # 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",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )

tests/e2e/multigpu/test_llama.py

@@ -14,7 +14,7 @@ from huggingface_hub import snapshot_download
 
 from axolotl.utils.dict import DictDefault
 
-from ..utils import with_temp_dir
+from ..utils import is_hopper, with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
@@ -59,7 +59,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -116,7 +116,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 50,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -144,6 +144,146 @@ class TestMultiGPULlama(unittest.TestCase):
             ]
         )
 
+    @pytest.mark.skipif(is_hopper(), reason="h100 doesn't support 8-bit lora")
+    @with_temp_dir
+    def test_dpo_lora_ddp(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "TinyLlama/TinyLlama_v1.1",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 2048,
+                "sample_packing": False,
+                "eval_sample_packing": False,
+                "pad_to_sequence_len": True,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.05,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "rl": "dpo",
+                "chat_template": "llama3",
+                "datasets": [
+                    {
+                        "path": "fozziethebeat/alpaca_messages_2k_dpo_test",
+                        "type": "chat_template.default",
+                        "field_messages": "conversation",
+                        "field_chosen": "chosen",
+                        "field_rejected": "rejected",
+                        "message_field_role": "role",
+                        "message_field_content": "content",
+                        "roles": {
+                            "system": ["system"],
+                            "user": ["user"],
+                            "assistant": ["assistant"],
+                        },
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 15,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "warmup_steps": 0,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+            }
+        )
+
+        # 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",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )
+
+    @with_temp_dir
+    def test_dpo_qlora_ddp(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM-135M",
+                "sequence_len": 2048,
+                "sample_packing": False,
+                "eval_sample_packing": False,
+                "pad_to_sequence_len": True,
+                "load_in_4bit": True,
+                "adapter": "qlora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.05,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "rl": "dpo",
+                "chat_template": "chatml",
+                "datasets": [
+                    {
+                        "path": "fozziethebeat/alpaca_messages_2k_dpo_test",
+                        "type": "chat_template.default",
+                        "field_messages": "conversation",
+                        "field_chosen": "chosen",
+                        "field_rejected": "rejected",
+                        "message_field_role": "role",
+                        "message_field_content": "content",
+                        "roles": {
+                            "system": ["system"],
+                            "user": ["user"],
+                            "assistant": ["assistant"],
+                        },
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 15,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "warmup_steps": 0,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+            }
+        )
+
+        # 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",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )
+
     @with_temp_dir
     def test_fsdp(self, temp_dir):
         # pylint: disable=duplicate-code
@@ -165,7 +305,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -231,7 +371,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -273,7 +413,6 @@ class TestMultiGPULlama(unittest.TestCase):
             ]
         )
 
-    @pytest.mark.skip("disabled due to upstream issue")
     @with_temp_dir
     def test_fsdp_qlora_prequant_packed(self, temp_dir):
         # pylint: disable=duplicate-code
@@ -282,6 +421,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "base_model": "axolotl-ai-co/TinyLlama_v1.1-bnb-nf4-bf16",
                 "tokenizer_type": "AutoTokenizer",
                 "adapter": "qlora",
+                "mean_resizing_embeddings": True,
                 "load_in_4bit": True,
                 "lora_r": 8,
                 "lora_alpha": 16,
@@ -297,7 +437,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "pad_token": "<|end_of_text|>",
+                    "pad_token": "</s>",
                 },
                 "datasets": [
                     {
@@ -307,7 +447,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -373,7 +513,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -432,7 +572,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,

tests/e2e/multigpu/test_qwen2.py

@@ -47,7 +47,7 @@ class TestMultiGPUQwen2(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 100,
+                "max_steps": 15,
                 "warmup_steps": 20,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 2,

tests/e2e/patched/test_4d_multipack_llama.py

@@ -13,7 +13,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
-from ..utils import require_torch_2_1_1, with_temp_dir
+from ..utils import require_torch_2_3_1, with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
@@ -24,7 +24,7 @@ class Test4dMultipackLlama(unittest.TestCase):
     Test case for Llama models using 4d attention with multipack
     """
 
-    @require_torch_2_1_1
+    @require_torch_2_3_1
     @with_temp_dir
     def test_sdp_lora_packing(self, temp_dir):
         # pylint: disable=duplicate-code

tests/e2e/patched/test_unsloth_integration.py

@@ -1,22 +1,12 @@
 """Test module for checking whether the integration of Unsloth with Hugging Face Transformers is working as expected."""
 import unittest
 
-from axolotl.monkeypatch.unsloth_ import (
-    check_cel_is_patchable,
-    check_self_attn_is_patchable,
-)
+from axolotl.monkeypatch.unsloth_ import check_self_attn_is_patchable
 
 
 class TestUnslothIntegration(unittest.TestCase):
     """Unsloth monkeypatch integration tests."""
 
-    def test_is_cel_patchable(self):
-        # ensures the current version of transformers has loss code that matches our patching code
-        self.assertTrue(
-            check_cel_is_patchable(),
-            "HF transformers loss code has changed and isn't patchable",
-        )
-
     def test_is_self_attn_patchable(self):
         # ensures the current version of transformers has loss code that matches our patching code
         self.assertTrue(

tests/e2e/test_dpo.py

@@ -115,6 +115,51 @@ class TestDPOLlamaLora(unittest.TestCase):
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "checkpoint-20/adapter_model.safetensors").exists()
 
+    @with_temp_dir
+    def test_dpo_use_weighting(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 64,
+                "lora_alpha": 32,
+                "lora_dropout": 0.1,
+                "lora_target_linear": True,
+                "special_tokens": {},
+                "rl": "dpo",
+                "dpo_use_weighting": True,
+                "datasets": [
+                    {
+                        "path": "arcee-ai/distilabel-intel-orca-dpo-pairs-binarized",
+                        "type": "chatml.ultra",
+                        "split": "train",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "paged_adamw_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "warmup_steps": 5,
+                "gradient_checkpointing": True,
+                "gradient_checkpointing_kwargs": {"use_reentrant": True},
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "checkpoint-20/adapter_model.safetensors").exists()
+
     @pytest.mark.skip("kto_pair no longer supported in trl")
     @with_temp_dir
     def test_kto_pair_lora(self, temp_dir):

tests/e2e/test_load_model.py

@@ -0,0 +1,95 @@
+"""Module for testing ModelLoader."""
+
+import shutil
+import tempfile
+
+import pytest
+import torch
+
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.models import ModelLoader, load_model, load_tokenizer
+
+
+@pytest.fixture(name="temp_dir")
+def fixture_temp_dir():
+    temp_dir = tempfile.mkdtemp()
+    yield temp_dir
+    shutil.rmtree(temp_dir)
+
+
+class TestLoadModelUtils:
+    """
+    Testing module testing ModelLoader.
+    """
+
+    def setup_method(self):
+        # load config
+        self.cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "tokenizer_config": "JackFram/llama-68m",
+                "sequence_len": 1024,
+                "load_in_8bit": False,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "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,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+            }
+        )
+        self.model_loader = (  # pylint: disable=attribute-defined-outside-init
+            ModelLoader(
+                cfg=self.cfg,
+                tokenizer="",
+            )
+        )
+
+    @pytest.mark.parametrize("embedding_modules", ["embed_tokens", "lm_head"])
+    @pytest.mark.parametrize(
+        "dist_dtype", [torch.bfloat16, torch.float16, torch.float32]
+    )
+    @pytest.mark.parametrize("before_kbit_train_or_finetune", [True, False])
+    def test_convert_embedding_modules_dtype(
+        self, temp_dir, embedding_modules, dist_dtype, before_kbit_train_or_finetune
+    ):
+        self.cfg.output_dir = temp_dir
+        self.model_loader.tokenizer = load_tokenizer(self.cfg)  # pylint: disable=all
+        self.model_loader.model, _ = load_model(
+            self.cfg,
+            self.model_loader.tokenizer,
+            inference=False,
+            reference_model=True,
+        )
+        self.model_loader.convert_embedding_modules_dtype(
+            embedding_modules, dist_dtype, before_kbit_train_or_finetune
+        )
+        for name, module in self.model_loader.model.named_modules():
+            if (
+                "norm" in name
+                or (before_kbit_train_or_finetune and name.endswith(".gate"))
+                or (
+                    any(m in name for m in embedding_modules)
+                    and hasattr(module, "weight")
+                )
+            ):
+                for _, param in module.named_parameters():
+                    assert param.dtype == dist_dtype

tests/e2e/test_packing_loss.py

@@ -0,0 +1,74 @@
+"""
+E2E tests for packed training
+"""
+
+import logging
+import os
+import unittest
+
+from tbparse import SummaryReader
+from transformers.utils import is_torch_bf16_gpu_available
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import most_recent_subdir, with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestPackedLlama(unittest.TestCase):
+    """
+    Test case for Packed training of llama models
+    """
+
+    @with_temp_dir
+    def test_loss_packed(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM-135M",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "val_set_size": 0.0,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "vicgalle/alpaca-gpt4",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "max_steps": 5,
+                "use_tensorboard": True,
+            }
+        )
+        if is_torch_bf16_gpu_available():
+            cfg.bf16 = True
+        else:
+            cfg.fp16 = True
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+
+        tb_log_path = most_recent_subdir(temp_dir + "/runs")
+        event_file = os.path.join(tb_log_path, sorted(os.listdir(tb_log_path))[0])
+        reader = SummaryReader(event_file)
+        df = reader.scalars  # pylint: disable=invalid-name
+        df = df[(df.tag == "train/train_loss")]  # pylint: disable=invalid-name
+        assert df.value.values[-1] < 2.0, "Loss is too high"

tests/e2e/utils.py

@@ -9,6 +9,8 @@ from functools import wraps
 from importlib.metadata import version
 from pathlib import Path
 
+import torch
+
 
 def with_temp_dir(test_func):
     @wraps(test_func)
@@ -35,13 +37,18 @@ def most_recent_subdir(path):
     return subdir
 
 
-def require_torch_2_1_1(test_case):
+def require_torch_2_3_1(test_case):
     """
-    Decorator marking a test that requires torch >= 2.1.1
+    Decorator marking a test that requires torch >= 2.3.1
     """
 
-    def is_min_2_1_1():
+    def is_min_2_3_1():
         torch_version = version("torch")
-        return torch_version >= "2.1.1"
+        return torch_version >= "2.3.1"
 
-    return unittest.skipUnless(is_min_2_1_1(), "test torch 2.1.1")(test_case)
+    return unittest.skipUnless(is_min_2_3_1(), "test torch 2.3.1")(test_case)
+
+
+def is_hopper():
+    compute_capability = torch.cuda.get_device_capability()
+    return compute_capability == (9, 0)

tests/prompt_strategies/test_chat_template_utils.py

@@ -0,0 +1,125 @@
+"""
+Tests for utils in axolotl.utils.chat_templates
+"""
+import unittest
+
+import pytest
+from transformers import AutoTokenizer
+
+from axolotl.utils.chat_templates import (
+    _CHAT_TEMPLATES,
+    extract_chat_template_args,
+    get_chat_template,
+)
+
+
+@pytest.fixture(name="llama3_tokenizer")
+def fixture_llama3_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B")
+
+    return tokenizer
+
+
+class TestGetChatTemplateUtils:
+    """
+    Tests the get_chat_template function.
+    """
+
+    def test_known_chat_template(self):
+        chat_template_str = get_chat_template("llama3")
+        assert chat_template_str == _CHAT_TEMPLATES["llama3"]
+
+    def test_invalid_chat_template(self):
+        with pytest.raises(ValueError) as exc:
+            get_chat_template("invalid_template")
+            assert str(exc) == "Template 'invalid_template' not found."
+
+    def test_tokenizer_default_no_tokenizer(self):
+        with pytest.raises(ValueError):
+            get_chat_template("tokenizer_default", tokenizer=None)
+
+    def test_tokenizer_default_no_chat_template_on_tokenizer(self, llama3_tokenizer):
+        with pytest.raises(ValueError):
+            get_chat_template("tokenizer_default", tokenizer=llama3_tokenizer)
+
+    def test_tokenizer_default_with_chat_template_on_tokenizer(self, llama3_tokenizer):
+        llama3_tokenizer.chat_template = "test_template"
+        chat_template_str = get_chat_template(
+            "tokenizer_default", tokenizer=llama3_tokenizer
+        )
+        assert chat_template_str == "test_template"
+
+    def test_tokenizer_default_fallback_no_tokenizer(self):
+        with pytest.raises(ValueError):
+            get_chat_template("tokenizer_default_fallback_test", tokenizer=None)
+
+    def test_tokenizer_default_fallback_no_chat_template_on_tokenizer(
+        self, llama3_tokenizer
+    ):
+        chat_template_str = get_chat_template(
+            "tokenizer_default_fallback_chatml", tokenizer=llama3_tokenizer
+        )
+        assert chat_template_str == get_chat_template("chatml")
+
+    def test_tokenizer_default_fallback_with_chat_template_on_tokenizer(
+        self, llama3_tokenizer
+    ):
+        llama3_tokenizer.chat_template = "test_template"
+        chat_template_str = get_chat_template(
+            "tokenizer_default_fallback_chatml", tokenizer=llama3_tokenizer
+        )
+        assert chat_template_str == "test_template"
+
+    def test_jinja_template_mode(self):
+        jinja_template = "example_jinja_template"
+        chat_template_str = get_chat_template("jinja", jinja_template=jinja_template)
+        assert chat_template_str == jinja_template
+
+    def test_jinja_template_mode_no_jinja_template(self):
+        with pytest.raises(ValueError):
+            get_chat_template("jinja", jinja_template=None)
+
+    def test_extract_chat_template_args(self):
+        # No ds_cfg
+        chat_template_choice, chat_template_jinja = extract_chat_template_args(
+            cfg={"chat_template": "chatml"},
+        )
+        assert chat_template_choice == "chatml"
+        assert chat_template_jinja is None
+
+        # ds_cfg provided
+        chat_template_choice, chat_template_jinja = extract_chat_template_args(
+            cfg={
+                "chat_template": "jinja",
+                "chat_template_jinja": "global_jinja_template",
+            },
+            ds_cfg={"chat_template": "llama3", "chat_template_jinja": None},
+        )
+        assert chat_template_choice == "llama3"
+        assert chat_template_jinja is None
+
+        # ds_cfg provided with jinja template
+        chat_template_choice, chat_template_jinja = extract_chat_template_args(
+            cfg={"chat_template": "chatml", "chat_template_jinja": None},
+            ds_cfg={
+                "chat_template": "jinja",
+                "chat_template_jinja": "ds_jinja_template",
+            },
+        )
+        assert chat_template_choice == "jinja"
+        assert chat_template_jinja == "ds_jinja_template"
+
+        # ds_cfg provided with no chat_template
+        chat_template_choice, chat_template_jinja = extract_chat_template_args(
+            cfg={
+                "chat_template": "jinja",
+                "chat_template_jinja": "global_jinja_template",
+            },
+            ds_cfg={"chat_template": None, "chat_template_jinja": "ds_jinja_template"},
+        )
+        assert chat_template_choice == "jinja"
+        assert chat_template_jinja == "global_jinja_template"
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/prompt_strategies/test_chat_templates.py

@@ -11,7 +11,7 @@ from axolotl.prompt_strategies.chat_template import (
     load,
 )
 from axolotl.prompters import IGNORE_TOKEN_ID
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template
 from axolotl.utils.dict import DictDefault
 
 logging.basicConfig(level=logging.DEBUG)
@@ -73,7 +73,7 @@ class TestAssistantChatTemplateLlama3:
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
                 llama3_tokenizer,
-                chat_template=chat_templates("llama3"),
+                chat_template=get_chat_template("llama3"),
                 message_field_role="role",
                 message_field_content="content",
                 roles={
@@ -113,7 +113,7 @@ class TestAssistantChatTemplateLlama3:
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
                 phi35_tokenizer,
-                chat_template=chat_templates("phi_35"),
+                chat_template=get_chat_template("phi_35"),
                 message_field_role="role",
                 message_field_content="content",
                 roles={
@@ -171,7 +171,7 @@ class TestAssistantChatTemplateLlama3:
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
                 llama3_tokenizer,
-                chat_template=chat_templates("llama3"),
+                chat_template=get_chat_template("llama3"),
                 message_field_role="role",
                 message_field_content="content",
                 message_field_training="training",
@@ -230,7 +230,7 @@ class TestSharegptChatTemplateLlama3:
         # pylint: disable=duplicate-code
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -283,7 +283,7 @@ class TestSharegptChatTemplateLlama3:
         # pylint: disable=duplicate-code
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -336,7 +336,7 @@ class TestSharegptChatTemplateLlama3:
         # pylint: disable=duplicate-code
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,

tests/prompt_strategies/test_chat_templates_advanced.py

@@ -12,7 +12,7 @@ from axolotl.prompt_strategies.chat_template import (
     ChatTemplateStrategy,
 )
 from axolotl.prompters import IGNORE_TOKEN_ID
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template
 
 logging.basicConfig(level=logging.DEBUG)
 LOG = logging.getLogger("axolotl")
@@ -35,7 +35,7 @@ class TestChatTemplateConfigurations:
         LOG.info("Testing with train_on_inputs=True")
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=True,
@@ -80,7 +80,7 @@ class TestChatTemplateConfigurations:
         LOG.info("Testing with train_on_inputs=False")
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -123,7 +123,7 @@ class TestChatTemplateConfigurations:
         LOG.info("Testing roles_to_train with assistant only")
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -151,7 +151,7 @@ class TestChatTemplateConfigurations:
         LOG.info("Testing roles_to_train with all roles")
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=True,
@@ -184,7 +184,7 @@ class TestChatTemplateConfigurations:
         LOG.info("Testing with empty roles_to_train")
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -205,7 +205,7 @@ class TestChatTemplateConfigurations:
         LOG.info("Testing with train_on_eos='all'")
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -232,7 +232,7 @@ class TestChatTemplateConfigurations:
         LOG.info("Testing with train_on_eos='turn'")
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -282,7 +282,7 @@ class TestChatTemplateConfigurations:
         LOG.info("Testing with train_on_eos='last'")
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -315,7 +315,7 @@ class TestChatTemplateConfigurations:
         LOG.info("Testing with train_on_eos='none'")
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_template=chat_templates("llama3")
+                llama3_tokenizer, chat_template=get_chat_template("llama3")
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -343,7 +343,7 @@ class TestChatTemplateConfigurations:
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
                 llama3_tokenizer,
-                chat_template=chat_templates("llama3"),
+                chat_template=get_chat_template("llama3"),
                 drop_system_message=True,
             ),
             tokenizer=llama3_tokenizer,
@@ -371,7 +371,7 @@ class TestChatTemplateConfigurations:
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
                 llama3_tokenizer,
-                chat_template=chat_templates("llama3"),
+                chat_template=get_chat_template("llama3"),
                 roles=custom_roles,
             ),
             tokenizer=llama3_tokenizer,
@@ -424,7 +424,7 @@ class TestChatTemplateConfigurations:
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
                 llama3_tokenizer,
-                chat_template=chat_templates("llama3"),
+                chat_template=get_chat_template("llama3"),
                 message_field_training="train",
                 message_field_training_detail="train_detail",
             ),

tests/prompt_strategies/test_dpo_chat_templates.py

@@ -86,6 +86,20 @@ def fixture_llama3_tokenizer():
     return tokenizer
 
 
+@pytest.fixture(name="phi3_tokenizer")
+def fixture_phi3_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-medium-128k-instruct")
+
+    return tokenizer
+
+
+@pytest.fixture(name="gemma_tokenizer")
+def fixture_gemma_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("unsloth/gemma-2b-it", revision="703fb4a")
+
+    return tokenizer
+
+
 class TestAssistantDPOChatTemplateLlama3:
     """
     Test class for assistant style datasets with llama-3 prompts using the chat_template strategy.
@@ -99,7 +113,7 @@ class TestAssistantDPOChatTemplateLlama3:
                     "chat_template": "llama3",
                     "datasets": [
                         {
-                            "chat_template": "llama3",
+                            "type": "chat_template",
                         }
                     ],
                 }
@@ -124,7 +138,7 @@ class TestAssistantDPOChatTemplateLlama3:
                     "chat_template": "llama3",
                     "datasets": [
                         {
-                            "chat_template": "llama3",
+                            "type": "chat_template",
                             "field_messages": "conversation",
                             "field_chosen": "better",
                             "field_rejected": "worse",
@@ -152,5 +166,65 @@ class TestAssistantDPOChatTemplateLlama3:
         assert result["rejected"] == "party on<|eot_id|>"
 
 
+class TestAssistantDPOChatTemplatePhi3:
+    """
+    Test class for assistant style datasets with phi-3 prompts using the tokenizer's chat_template strategy.
+    """
+
+    def test_phi3_defaults(self, phi3_tokenizer, assistant_dataset):
+        # pylint: disable=duplicate-code
+        transform_fn = default(
+            DictDefault(
+                {
+                    "chat_template": "tokenizer_default",
+                    "datasets": [
+                        {
+                            "type": "chat_template",
+                        }
+                    ],
+                }
+            )
+        )
+        result = transform_fn(assistant_dataset[0], tokenizer=phi3_tokenizer)
+        assert result["prompt"] == (
+            "<|user|>\nhello<|end|>\n"
+            + "<|assistant|>\nhello<|end|>\n"
+            + "<|user|>\ngoodbye<|end|>\n"
+            + "<|assistant|>\n"
+        )
+        assert result["chosen"] == "goodbye<|end|>"
+        assert result["rejected"] == "party on<|end|>"
+
+
+class TestAssistantDPOChatTemplateGemma:
+    """
+    Test class for assistant style datasets with gemma prompts using the tokenizer's chat_template strategy.
+    """
+
+    def test_gemma_defaults(self, gemma_tokenizer, assistant_dataset):
+        # pylint: disable=duplicate-code
+        transform_fn = default(
+            DictDefault(
+                {
+                    "chat_template": "tokenizer_default",
+                    "datasets": [
+                        {
+                            "type": "chat_template",
+                        }
+                    ],
+                }
+            )
+        )
+        result = transform_fn(assistant_dataset[0], tokenizer=gemma_tokenizer)
+        assert result["prompt"] == (
+            "<bos><start_of_turn>user\nhello<end_of_turn>\n"
+            + "<start_of_turn>model\nhello<end_of_turn>\n"
+            + "<start_of_turn>user\ngoodbye<end_of_turn>\n"
+            + "<start_of_turn>model\n"
+        )
+        assert result["chosen"] == "goodbye<end_of_turn>"
+        assert result["rejected"] == "party on<end_of_turn>"
+
+
 if __name__ == "__main__":
     unittest.main()

tests/test_datasets.py

@@ -367,43 +367,44 @@ class TestDatasetPreparation(unittest.TestCase):
     def test_load_local_hub_with_revision(self):
         """Verify that a local copy of a hub dataset can be loaded with a specific revision"""
         with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_ds_path = Path("mhenrichsen/alpaca_2k_test")
-            tmp_ds_path.mkdir(parents=True, exist_ok=True)
-            snapshot_download(
-                repo_id="mhenrichsen/alpaca_2k_test",
-                repo_type="dataset",
-                local_dir=tmp_ds_path,
-                revision="d05c1cb",
-            )
+            with tempfile.TemporaryDirectory() as tmp_dir2:
+                tmp_ds_path = Path(tmp_dir2) / "mhenrichsen/alpaca_2k_test"
+                tmp_ds_path.mkdir(parents=True, exist_ok=True)
+                snapshot_download(
+                    repo_id="mhenrichsen/alpaca_2k_test",
+                    repo_type="dataset",
+                    local_dir=tmp_ds_path,
+                    revision="d05c1cb",
+                )
 
-            prepared_path = Path(tmp_dir) / "prepared"
-            cfg = DictDefault(
-                {
-                    "tokenizer_config": "huggyllama/llama-7b",
-                    "sequence_len": 1024,
-                    "datasets": [
-                        {
-                            "path": "mhenrichsen/alpaca_2k_test",
-                            "ds_type": "parquet",
-                            "type": "alpaca",
-                            "data_files": [
-                                "mhenrichsen/alpaca_2k_test/alpaca_2000.parquet",
-                            ],
-                            "revision": "d05c1cb",
-                        },
-                    ],
-                }
-            )
+                prepared_path = Path(tmp_dir) / "prepared"
+                cfg = DictDefault(
+                    {
+                        "tokenizer_config": "huggyllama/llama-7b",
+                        "sequence_len": 1024,
+                        "datasets": [
+                            {
+                                "path": "mhenrichsen/alpaca_2k_test",
+                                "ds_type": "parquet",
+                                "type": "alpaca",
+                                "data_files": [
+                                    f"{tmp_ds_path}/alpaca_2000.parquet",
+                                ],
+                                "revision": "d05c1cb",
+                            },
+                        ],
+                    }
+                )
 
-            dataset, _ = load_tokenized_prepared_datasets(
-                self.tokenizer, cfg, prepared_path
-            )
+                dataset, _ = load_tokenized_prepared_datasets(
+                    self.tokenizer, cfg, prepared_path
+                )
 
-            assert len(dataset) == 2000
-            assert "input_ids" in dataset.features
-            assert "attention_mask" in dataset.features
-            assert "labels" in dataset.features
-            shutil.rmtree(tmp_ds_path)
+                assert len(dataset) == 2000
+                assert "input_ids" in dataset.features
+                assert "attention_mask" in dataset.features
+                assert "labels" in dataset.features
+                shutil.rmtree(tmp_ds_path)
 
 
 if __name__ == "__main__":

tests/test_validation.py

@@ -13,6 +13,7 @@ from axolotl.utils import is_comet_available
 from axolotl.utils.config import validate_config
 from axolotl.utils.config.models.input.v0_4_1 import AxolotlConfigWCapabilities
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.models import check_model_config
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
@@ -1432,3 +1433,58 @@ class TestValidationComet(BaseValidation):
 
         for key in comet_env.keys():
             os.environ.pop(key, None)
+
+
+class TestValidationMLflow(BaseValidation):
+    """
+    Validation test for MLflow
+    """
+
+    def test_hf_mlflow_artifacts_config_sets_env(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "hf_mlflow_log_artifacts": True,
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+
+        assert new_cfg.hf_mlflow_log_artifacts is True
+
+        # Check it's not already present in env
+        assert "HF_MLFLOW_LOG_ARTIFACTS" not in os.environ
+
+        setup_mlflow_env_vars(new_cfg)
+
+        assert os.environ.get("HF_MLFLOW_LOG_ARTIFACTS") == "true"
+
+        os.environ.pop("HF_MLFLOW_LOG_ARTIFACTS", None)
+
+    def test_mlflow_not_used_by_default(self, minimal_cfg):
+        cfg = DictDefault({}) | minimal_cfg
+
+        new_cfg = validate_config(cfg)
+
+        setup_mlflow_env_vars(new_cfg)
+
+        assert cfg.use_mlflow is not True
+
+        cfg = (
+            DictDefault(
+                {
+                    "mlflow_experiment_name": "foo",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+
+        setup_mlflow_env_vars(new_cfg)
+
+        assert new_cfg.use_mlflow is True
+
+        os.environ.pop("MLFLOW_EXPERIMENT_NAME", None)

tests/test_validation_dataset.py

@@ -0,0 +1,238 @@
+"""Module for testing the validation module for the dataset config"""
+
+import warnings
+from typing import Optional
+
+import pytest
+
+from axolotl.utils.config import validate_config
+from axolotl.utils.config.models.input.v0_4_1 import ChatTemplate
+from axolotl.utils.dict import DictDefault
+
+warnings.filterwarnings("error")
+
+
+@pytest.fixture(name="minimal_cfg")
+def fixture_cfg():
+    return DictDefault(
+        {
+            "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
+            "learning_rate": 0.000001,
+            "micro_batch_size": 1,
+            "gradient_accumulation_steps": 1,
+        }
+    )
+
+
+# pylint: disable=too-many-public-methods (duplicate-code)
+class BaseValidation:
+    """
+    Base validation module to setup the log capture
+    """
+
+    _caplog: Optional[pytest.LogCaptureFixture] = None
+
+    @pytest.fixture(autouse=True)
+    def inject_fixtures(self, caplog):
+        self._caplog = caplog
+
+
+class TestValidationCheckDatasetConfig(BaseValidation):
+    """
+    Test the validation for the dataset config to ensure no correct parameters are dropped
+    """
+
+    def test_dataset_config_no_drop_param(self, minimal_cfg):
+        cfg = DictDefault(
+            minimal_cfg
+            | {
+                "datasets": [
+                    {
+                        "path": "LDJnr/Puffin",
+                        "type": "sharegpt",
+                        "conversation": "chatml",
+                        "shards": 10,
+                    }
+                ]
+            }
+        )
+
+        checked_cfg = validate_config(cfg)
+
+        def _check_config():
+            assert checked_cfg.datasets[0].path == cfg.datasets[0].path
+            assert checked_cfg.datasets[0].type == cfg.datasets[0].type
+            assert checked_cfg.datasets[0].conversation == cfg.datasets[0].conversation
+            assert checked_cfg.datasets[0].shards == cfg.datasets[0].shards
+
+        _check_config()
+
+        checked_cfg = validate_config(
+            cfg,
+            capabilities={
+                "bf16": "false",
+                "n_gpu": 1,
+                "compute_capability": "8.0",
+            },
+        )
+
+        _check_config()
+
+    def test_dataset_default_chat_template_no_drop_param(self, minimal_cfg):
+        cfg = DictDefault(
+            minimal_cfg
+            | {
+                "datasets": [
+                    {
+                        "path": "LDJnr/Puffin",
+                        "type": "chat_template",
+                        "field_messages": "conversations",
+                        "shards": 10,
+                        "message_field_role": "from",
+                        "message_field_content": "value",
+                    }
+                ],
+            }
+        )
+
+        checked_cfg = validate_config(cfg)
+
+        def _check_config():
+            assert checked_cfg.datasets[0].path == cfg.datasets[0].path
+            assert checked_cfg.datasets[0].type == cfg.datasets[0].type
+            assert checked_cfg.chat_template is None
+            assert (
+                checked_cfg.datasets[0].chat_template == ChatTemplate.tokenizer_default
+            )
+            assert (
+                checked_cfg.datasets[0].field_messages == cfg.datasets[0].field_messages
+            )
+            assert checked_cfg.datasets[0].shards == cfg.datasets[0].shards
+            assert (
+                checked_cfg.datasets[0].message_field_role
+                == cfg.datasets[0].message_field_role
+            )
+            assert (
+                checked_cfg.datasets[0].message_field_content
+                == cfg.datasets[0].message_field_content
+            )
+
+        _check_config()
+
+        checked_cfg = validate_config(
+            cfg,
+            capabilities={
+                "bf16": "false",
+                "n_gpu": 1,
+                "compute_capability": "8.0",
+            },
+        )
+
+        _check_config()
+
+    def test_dataset_partial_default_chat_template_no_drop_param(self, minimal_cfg):
+        cfg = DictDefault(
+            minimal_cfg
+            | {
+                "chat_template": "chatml",
+                "datasets": [
+                    {
+                        "path": "LDJnr/Puffin",
+                        "type": "chat_template",
+                        "field_messages": "conversations",
+                        "shards": 10,
+                        "message_field_role": "from",
+                        "message_field_content": "value",
+                    }
+                ],
+            }
+        )
+
+        checked_cfg = validate_config(cfg)
+
+        def _check_config():
+            assert checked_cfg.datasets[0].path == cfg.datasets[0].path
+            assert checked_cfg.datasets[0].type == cfg.datasets[0].type
+            assert checked_cfg.chat_template == ChatTemplate.chatml
+            assert (
+                checked_cfg.datasets[0].chat_template == ChatTemplate.tokenizer_default
+            )
+            assert (
+                checked_cfg.datasets[0].field_messages == cfg.datasets[0].field_messages
+            )
+            assert checked_cfg.datasets[0].shards == cfg.datasets[0].shards
+            assert (
+                checked_cfg.datasets[0].message_field_role
+                == cfg.datasets[0].message_field_role
+            )
+            assert (
+                checked_cfg.datasets[0].message_field_content
+                == cfg.datasets[0].message_field_content
+            )
+
+        _check_config()
+
+        checked_cfg = validate_config(
+            cfg,
+            capabilities={
+                "bf16": "false",
+                "n_gpu": 1,
+                "compute_capability": "8.0",
+            },
+        )
+
+        _check_config()
+
+    def test_dataset_chatml_chat_template_no_drop_param(self, minimal_cfg):
+        cfg = DictDefault(
+            minimal_cfg
+            | {
+                "chat_template": "chatml",
+                "datasets": [
+                    {
+                        "path": "LDJnr/Puffin",
+                        "type": "chat_template",
+                        "chat_template": "gemma",
+                        "field_messages": "conversations",
+                        "shards": 10,
+                        "message_field_role": "from",
+                        "message_field_content": "value",
+                    }
+                ],
+            }
+        )
+
+        checked_cfg = validate_config(cfg)
+
+        def _check_config():
+            assert checked_cfg.datasets[0].path == cfg.datasets[0].path
+            assert checked_cfg.datasets[0].type == cfg.datasets[0].type
+            assert checked_cfg.chat_template == cfg.chat_template
+            assert (
+                checked_cfg.datasets[0].chat_template == cfg.datasets[0].chat_template
+            )
+            assert (
+                checked_cfg.datasets[0].field_messages == cfg.datasets[0].field_messages
+            )
+            assert checked_cfg.datasets[0].shards == cfg.datasets[0].shards
+            assert (
+                checked_cfg.datasets[0].message_field_role
+                == cfg.datasets[0].message_field_role
+            )
+            assert (
+                checked_cfg.datasets[0].message_field_content
+                == cfg.datasets[0].message_field_content
+            )
+
+        _check_config()
+
+        checked_cfg = validate_config(
+            cfg,
+            capabilities={
+                "bf16": "false",
+                "n_gpu": 1,
+                "compute_capability": "8.0",
+            },
+        )
+
+        _check_config()

tests/utils/test_models.py

@@ -1,18 +1,64 @@
 """Module for testing models utils file."""
 
-
-import unittest
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch
 
 import pytest
+from transformers import BitsAndBytesConfig, PreTrainedTokenizerBase
+from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
+from transformers.utils.import_utils import is_torch_mps_available
 
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model
+from axolotl.utils.models import ModelLoader, load_model
 
 
-class ModelsUtilsTest(unittest.TestCase):
+class TestModelsUtils:
     """Testing module for models utils."""
 
+    def setup_method(self) -> None:
+        # load config
+        self.cfg = DictDefault(  # pylint: disable=attribute-defined-outside-init
+            {
+                "base_model": "JackFram/llama-68m",
+                "model_type": "LlamaForCausalLM",
+                "tokenizer_type": "LlamaTokenizer",
+                "load_in_8bit": True,
+                "load_in_4bit": False,
+                "adapter": "lora",
+                "flash_attention": False,
+                "sample_packing": True,
+                "device_map": "auto",
+            }
+        )
+        self.tokenizer = MagicMock(  # pylint: disable=attribute-defined-outside-init
+            spec=PreTrainedTokenizerBase
+        )
+        self.inference = False  # pylint: disable=attribute-defined-outside-init
+        self.reference_model = True  # pylint: disable=attribute-defined-outside-init
+
+        # init ModelLoader
+        self.model_loader = (  # pylint: disable=attribute-defined-outside-init
+            ModelLoader(
+                cfg=self.cfg,
+                tokenizer=self.tokenizer,
+                inference=self.inference,
+                reference_model=self.reference_model,
+            )
+        )
+
+    def test_set_device_map_config(self):
+        # check device_map
+        device_map = self.cfg.device_map
+        if is_torch_mps_available():
+            device_map = "mps"
+        self.model_loader.set_device_map_config()
+        if is_deepspeed_zero3_enabled():
+            assert "device_map" not in self.model_loader.model_kwargs
+        else:
+            assert device_map in self.model_loader.model_kwargs["device_map"]
+
+        # check torch_dtype
+        assert self.cfg.torch_dtype == self.model_loader.model_kwargs["torch_dtype"]
+
     def test_cfg_throws_error_with_s2_attention_and_sample_packing(self):
         cfg = DictDefault(
             {
@@ -35,3 +81,38 @@ class ModelsUtilsTest(unittest.TestCase):
                 "shifted-sparse attention does not currently support sample packing"
                 in str(exc.value)
             )
+
+    @pytest.mark.parametrize("adapter", ["lora", "qlora", None])
+    @pytest.mark.parametrize("load_in_8bit", [True, False])
+    @pytest.mark.parametrize("load_in_4bit", [True, False])
+    @pytest.mark.parametrize("gptq", [True, False])
+    def test_set_quantization_config(
+        self,
+        adapter,
+        load_in_8bit,
+        load_in_4bit,
+        gptq,
+    ):
+        # init cfg as args
+        self.cfg.load_in_8bit = load_in_8bit
+        self.cfg.load_in_4bit = load_in_4bit
+        self.cfg.gptq = gptq
+        self.cfg.adapter = adapter
+
+        self.model_loader.set_quantization_config()
+        if "quantization_config" in self.model_loader.model_kwargs or self.cfg.gptq:
+            assert not (
+                hasattr(self.model_loader.model_kwargs, "load_in_8bit")
+                and hasattr(self.model_loader.model_kwargs, "load_in_4bit")
+            )
+        elif load_in_8bit and self.cfg.adapter is not None:
+            assert self.model_loader.model_kwargs["load_in_8bit"]
+        elif load_in_4bit and self.cfg.adapter is not None:
+            assert self.model_loader.model_kwargs["load_in_4bit"]
+
+        if (self.cfg.adapter == "qlora" and load_in_4bit) or (
+            self.cfg.adapter == "lora" and load_in_8bit
+        ):
+            assert self.model_loader.model_kwargs.get(
+                "quantization_config", BitsAndBytesConfig
+            )