fix: total tokens and defaults in config

.github/workflows/docs.yml

@@ -12,9 +12,6 @@ jobs:
     build-deploy:
         runs-on: ubuntu-latest
         steps:
-        - name: cleanup node
-          run: |
-            sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL
         - name: Check out repository
           uses: actions/checkout@v4
         - name: Set up Quarto

.github/workflows/preview-docs.yml

@@ -11,7 +11,6 @@ on:
       - '_quarto.yml'
       - docs/scripts/generate_config_docs.py
       - src/axolotl/utils/schemas/**.py
-      - .github/workflows/preview-docs.yml
 
 permissions:
   checks: write
@@ -28,10 +27,6 @@ jobs:
     runs-on: ubuntu-latest
     if: ${{ !github.event.pull_request.draft }}
     steps:
-      - name: cleanup node
-        run: |
-          sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL
-
       - name: Check out repository
         uses: actions/checkout@v4
         with:

.github/workflows/tests.yml

@@ -66,12 +66,12 @@ jobs:
       - name: Check out repository code
         uses: actions/checkout@v4
 
-#      - name: Restore Cache from S3
-#        id: hf-cache-restore-s3
-#        run: |
-#          mkdir -p ~/.cache/huggingface/hub
-#          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd
-#
+      - name: Restore Cache from S3
+        id: hf-cache-restore-s3
+        run: |
+          mkdir -p /home/runner/.cache/huggingface/hub
+          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd
+
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
@@ -113,13 +113,9 @@ jobs:
 
       - name: Run tests
         run: |
-          df -h
-          pytest -v --durations=10 -n4 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
-          df -h
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
           pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
-          df -h
           pytest -v --durations=10 tests/patched/ --cov=axolotl --cov-append --cov-report=xml
-          df -h
           pytest -v --durations=10 tests/cli/ --cov=axolotl --cov-append --cov-report=xml
 
       - name: Upload coverage to Codecov
@@ -149,12 +145,12 @@ jobs:
       - name: Check out repository code
         uses: actions/checkout@v4
 
-#      - name: Restore Cache from S3
-#        id: hf-cache-restore-s3
-#        run: |
-#          mkdir -p ~/.cache/huggingface/hub
-#          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd
-#
+      - name: Restore Cache from S3
+        id: hf-cache-restore-s3
+        run: |
+          mkdir -p /home/runner/.cache/huggingface/hub
+          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd
+
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
@@ -192,11 +188,11 @@ jobs:
           axolotl --help
 
       - name: Show HF cache
-        run: hf cache scan
+        run: huggingface-cli scan-cache
 
       - name: Run tests
         run: |
-          pytest -v --durations=10 -n4 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
           pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
           pytest -v --durations=10 tests/cli/
 

.runpod/Dockerfile

@@ -10,7 +10,6 @@ ARG BASE_VOLUME="/runpod-volume"
 ENV BASE_VOLUME=$BASE_VOLUME
 ENV HF_DATASETS_CACHE="${BASE_VOLUME}/huggingface-cache/datasets"
 ENV HUGGINGFACE_HUB_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
-ENV HF_HUB_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
 ENV TRANSFORMERS_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
 
 COPY .runpod/src /src

README.md

@@ -29,7 +29,7 @@
 
 ## 🎉 Latest Updates
 
-- 2025/12: Axolotl now includes support for [Olmo3](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/olmo3), [Trinity](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/trinity), and [Ministral3](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/ministral3).
+- 2025/11: Axolotl now includes support for [Olmo3](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/olmo3).
 - 2025/10: New model support has been added in Axolotl for: [Qwen3 Next](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/qwen3-next), [Qwen2.5-vl, Qwen3-vl](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen2_5-vl), [Qwen3, Qwen3MoE](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3), [Granite 4](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/granite4), [HunYuan](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/hunyuan), [Magistral 2509](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral#vision), [Apertus](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/apertus), and [Seed-OSS](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/seed-oss).
 - 2025/09: Axolotl now has text diffusion training. Read more [here](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/diffusion).
 - 2025/08: QAT has been updated to include NVFP4 support. See [PR](https://github.com/axolotl-ai-cloud/axolotl/pull/3107).

examples/colab-notebooks/colab-axolotl-example.ipynb

@@ -40,7 +40,7 @@
     "%%capture\n",
     "# This step can take ~5-10 minutes to install dependencies\n",
     "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
-    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f643b88\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@5eff953\""
    ]
   },
   {
@@ -253,6 +253,7 @@
    "source": [
     "from axolotl.utils import set_pytorch_cuda_alloc_conf\n",
     "\n",
+    "# Set \"PYTORCH_CUDA_ALLOC_CONF\" env to save memory\n",
     "set_pytorch_cuda_alloc_conf()"
    ]
   },

examples/glm45/README.md

@@ -1,48 +0,0 @@
-# Finetune GLM4.5 with Axolotl
-
-[UNSTABLE]
-
-```bash
-# LoRA SFT (4xH200 @ 84GB/GPU)
-axolotl train examples/glm45/glm4.5-lora-fsdp2.yaml
-
-# FFT SFT (4xH200)
-# Checkpointing error on backward pass
-# Without checkpointing => OOM
-axolotl train examples/glm45/glm4.5-fft-fsdp2.yaml
-```
-
-## Dataset
-
-In addition to normal OpenAI Messages format, GLM4.5 support an extra parameter for thinking in assistant section.
-
-```json
-{
-    "role": "assistant",
-    "reasoning_content": "...",  // or have </think>...</think> in `content`
-    "content": "...",
-}
-```
-
-Note:
-- The role name for tools in this template is `tool`.
-- You will see this Axolotl WARNING. This is to be as expected as the template does not use EOS.
-```bash
-EOS token '<|endoftext|>' not found in chat_template. Please check if your template/EOS token is correct.
-```
-- Make sure you set the below extra attributes if needed
-```yaml
-datasets:
-  - path: ...
-    type: chat_template
-    message_property_mappings:
-      role: role
-      content: content
-
-    #   tool_calls: tool_calls  # uncomment if using tools
-    #   reasoning_content: reasoning_content  # uncomment if have reasoning
-
-# Uncomment if training on tool role (you would rarely if ever need this)
-# eot_tokens:
-#   - <|observation|>
-```

examples/glm45/glm4.5-fft-fsdp2.yaml

@@ -1,59 +0,0 @@
-base_model: zai-org/GLM-4.5-Air
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding
-
-datasets:
-  - path: winglian/pirate-ultrachat-10k
-    type: chat_template
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./outputs/qlora-out
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch_4bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-# gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-weight_decay: 0.0
-special_tokens:
-
-fsdp_version: 2
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Glm4MoeDecoderLayer
-  state_dict_type: SHARDED_STATE_DICT
-  reshard_after_forward: true
-  activation_checkpointing: true

examples/glm45/glm4.5-lora-fsdp2.yaml

@@ -1,74 +0,0 @@
-base_model: zai-org/GLM-4.5-Air
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding
-
-datasets:
-  - path: winglian/pirate-ultrachat-10k
-    type: chat_template
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./outputs/qlora-out
-
-adapter: lora
-lora_model_dir:
-
-lora_r: 16
-lora_alpha: 32
-lora_dropout: 0.05
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch_4bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-# gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-weight_decay: 0.0
-special_tokens:
-
-fsdp_version: 2
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Glm4MoeDecoderLayer
-  state_dict_type: SHARDED_STATE_DICT
-  reshard_after_forward: true
-  # activation_checkpointing: false

examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml

@@ -32,10 +32,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 2
 micro_batch_size: 1
 num_epochs: 1

examples/gpt-oss/gpt-oss-20b-fft-deepspeed-zero3.yaml

@@ -28,10 +28,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 2
 micro_batch_size: 1
 num_epochs: 1

examples/gpt-oss/gpt-oss-20b-fft-fsdp2-offload.yaml

@@ -29,10 +29,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 2
 micro_batch_size: 1
 num_epochs: 1

examples/gpt-oss/gpt-oss-20b-fft-fsdp2.yaml

@@ -28,10 +28,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 2
 micro_batch_size: 1
 num_epochs: 1

examples/gpt-oss/gpt-oss-20b-sft-lora-singlegpu.yaml

@@ -41,10 +41,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 8
 micro_batch_size: 1
 num_epochs: 1

examples/gpt-oss/gpt-oss-safeguard-20b-sft-lora-singlegpu.yaml

@@ -41,10 +41,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 8
 micro_batch_size: 1
 num_epochs: 1

examples/llama-3/3b-fp8-fsdp2.yaml

@@ -29,6 +29,7 @@ flex_attention: true
 flex_attn_compile_kwargs:
   dynamic: false
   mode: max-autotune-no-cudagraphs
+save_strategy: no
 torch_compile: true
 
 wandb_project:

examples/llama-3/diffusion/sft-1b.yaml

@@ -30,7 +30,7 @@ eval_sample_packing: true
 gradient_accumulation_steps: 4
 micro_batch_size: 4
 num_epochs: 1
-warmup_steps: 0.1
+warmup_ratio: 0.1
 
 optimizer: adamw_8bit
 lr_scheduler: cosine
@@ -44,7 +44,7 @@ resume_from_checkpoint:
 sdp_attention: true
 
 logging_steps: 1
-save_strategy: best
+save_strategy: epoch
 eval_strategy: epoch
 
 special_tokens:

examples/magistral/README.md

@@ -13,7 +13,7 @@ Thanks to the team at MistralAI for giving us early access to prepare for these
     Here is an example of how to install from pip:
 
 ```bash
-# Ensure you have Pytorch installed (Pytorch 2.7.0 min)
+# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
 pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```

examples/ministral/README.md

@@ -1,50 +0,0 @@
-# Finetune Ministral with Axolotl
-
-Ministral is a family of openweight models from MistralAI found on [HuggingFace](mistralai/Ministral-8B-Instruct-2410). This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-3. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/ministral/ministral-small-qlora.yaml
-    ```
-
-This config uses about 8.76 GiB VRAM.
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### Tips
-
-- We recommend adding the same/similar SystemPrompt that the model is tuned for. You can find this within the repo's files titled `SYSTEM_PROMPT.txt`.
-- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-- The text dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Limitations
-
-We only support the `mistral-common` tokenizer for Supervised Fine-tuning at the moment and for `type: chat_template` only.
-
-In addition, we do not support overriding tokens yet.
-
-## Related Resources
-
-- [MistralAI Ministral Blog](https://mistral.ai/news/ministraux)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
-
-
-## Future Work
-
-- Add parity to Preference Tuning, RL, etc.
-- Add parity to other tokenizer configs like overriding tokens.

examples/ministral/ministral-small-qlora.yaml

@@ -1,67 +0,0 @@
-base_model: mistralai/Ministral-8B-Instruct-2410
-
-# Enable to use mistral-common tokenizer
-tokenizer_use_mistral_common: true
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-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
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/ministral3/README.md

@@ -1,79 +0,0 @@
-# Finetune Ministral3 with Axolotl
-
-Ministral3 is a family of open-weight models from MistralAI found on [HuggingFace](https://huggingface.co/collections/mistralai/ministral-3). This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-Please see [Thinking](#thinking) and [Vision](#vision) for their respective fine-tuning.
-
-Thanks to the team at MistralAI for giving us early access to prepare for these releases.
-
-Note: This is still experimental given it is based on transformers v5 RC.
-
-## Getting started
-
-1. Install Axolotl from source following the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-3. Swap to the Axolotl transformers v5 branch
-
-    ```bash
-    cp examples/ministral3/ministral3-3b-qlora.yaml ministral3-3b-qlora.yaml
-
-    git fetch
-    git checkout transformers-v5
-
-    # Install packages for transformers v5
-    pip install -e .
-    ```
-
-4. Run the fine-tuning:
-
-    ```bash
-    axolotl train ministral3-3b-qlora.yaml
-    ```
-
-Let us know how it goes. Happy finetuning! 🚀
-
-
-### Tips
-
-- We recommend adding the same/similar SystemPrompt that the model is tuned for. You can find this within the repo's files titled `SYSTEM_PROMPT.txt`.
-- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-- The text dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-### Thinking
-
-Ministral3 2512 model supports thinking capabilities, enabling Chain-of-Thought reasoning with explicit thinking steps.
-
-📚 **[See the Thinking fine-tuning guide →](./think/README.md)**
-
-### Vision
-
-Ministral3 2512 model also supports vision capabilities.
-
-📚 **[See the Vision fine-tuning guide →](./vision/README.md)**
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Limitations
-
-We only support the `mistral-common` tokenizer for Supervised Fine-tuning at the moment and for `type: chat_template` only.
-
-In addition, we do not support overriding tokens yet.
-
-## Related Resources
-
-- [MistralAI Mistral3 Blog](https://mistral.ai/news/mistral-3)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
-
-
-## Future Work
-
-- Add parity to Preference Tuning, RL, etc.
-- Add parity to other tokenizer configs like overriding tokens.

examples/ministral3/ministral3-3b-qlora.yaml

@@ -1,67 +0,0 @@
-base_model: mistralai/Ministral-3-3B-Reasoning-2512
-
-# Enable to use mistral-common tokenizer
-tokenizer_use_mistral_common: true
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-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
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/ministral3/think/README.md

@@ -1,73 +0,0 @@
-# Ministral3 2512 Thinking Fine-tuning
-
-This guide covers fine-tuning [Ministral3 2512](https://huggingface.co/collections/mistralai/ministral-3) with thinking capabilities using Axolotl. The thinking model enables explicit Chain-of-Thought reasoning with separate thinking and response sections.
-
-## Prerequisites
-
-Before starting, ensure you have:
-- Installed Axolotl (see [main README](../README.md))
-
-## Getting Started
-
-Run the thinking model fine-tuning:
-
-```bash
-axolotl train examples/ministral3/think/ministral3-3b-think-qlora.yaml
-```
-
-This config uses about 4.76 GiB VRAM.
-
-### Tips
-
-- Dataset uses multi-content format with `type: thinking` support. See [Dataset Format](#dataset-format) below.
-- You cannot mix `content: str` and `content: list[dict]`, otherwise, dataset loading will fail. Keep it consistent.
-
-## Dataset Format
-
-The thinking model requires the multi-content dataset format with support for an extra `role: thinking` within system and assistant messages.
-
-Example format:
-
-```json
-{
-    "messages": [
-        {
-            "role": "system",
-            "content": [
-                { "type": "text", "text": "{SYSTEM_PROMPT}"}
-            ]
-        },
-        {
-            "role": "user",
-            "content": [
-                { "type": "text", "text": "Solve this step by step: What is 15% of 240?"}
-            ]
-        },
-        {
-            "role": "assistant",
-            "content": [
-                {
-                    "type": "thinking",
-                    "thinking": "I need to calculate 15% of 240. First, I'll convert 15% to decimal: 0.15. Then multiply: 0.15 × 240 = 36."
-                },
-                {
-                    "type": "text",
-                    "text": "To find 15% of 240, I'll multiply 240 by 0.15:\n\n240 × 0.15 = 36\n\nTherefore, 15% of 240 is 36."
-                }
-            ]
-        }
-    ]
-}
-```
-
-### Advanced Options
-
-The `thinking` section supports an optional `closed` parameter:
-
-```json
-{
-    "type": "thinking",
-    "thinking": "Internal reasoning here...",
-    "closed": true  // Default: true, controls adding the closing [/THINK] tag
-}
-```

examples/ministral3/think/ministral3-3b-think-qlora.yaml

@@ -1,67 +0,0 @@
-base_model: mistralai/Ministral-3-3B-Reasoning-2512
-
-# Enable to use mistral-common tokenizer
-tokenizer_use_mistral_common: true
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: Nanobit/text-think-2k-test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-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
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/ministral3/vision/README.md

@@ -1,57 +0,0 @@
-# Ministral3 2512 Vision Fine-tuning
-
-This guide covers fine-tuning [Ministral3 2512](https://huggingface.co/collections/mistralai/ministral-3) with vision capabilities using Axolotl.
-
-## Prerequisites
-
-Before starting, ensure you have:
-- Installed Axolotl from source (see [main README](../README.md#getting-started))
-
-## Getting started
-
-1. Install the required vision lib:
-    ```bash
-    pip install 'mistral-common[opencv]==1.8.6'
-    ```
-
-2. Download the example dataset image:
-   ```bash
-   wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
-   ```
-
-3. Run the fine-tuning:
-   ```bash
-   axolotl train examples/ministral3/vision/ministral3-3b-vision-qlora.yml
-   ```
-
-WARNING: The loss and grad norm will be much higher than normal at first. We suspect this to be inherent to the model as of the moment. If anyone would like to submit a fix for this, we are happy to take a look.
-
-### Tips
-
-Key differences from text-only model:
-- Multi-modal dataset format required
-- Sample packing not supported
-
-## Dataset Format
-
-The vision model requires multi-modal dataset format as documented [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
-
-One exception is that, passing `"image": PIL.Image` is not supported. MistralTokenizer only supports `path`, `url`, and `base64` for now.
-
-Example:
-```json
-{
-    "messages": [
-        {"role": "system", "content": [{ "type": "text", "text": "{SYSTEM_PROMPT}"}]},
-        {"role": "user", "content": [
-            { "type": "text", "text": "What's in this image?"},
-            {"type": "image", "path": "path/to/image.jpg" }
-        ]},
-        {"role": "assistant", "content": [{ "type": "text", "text": "..." }]},
-    ],
-}
-```
-
-## Limitations
-
-- Sample Packing is not supported for multi-modality training currently.

examples/ministral3/vision/ministral3-3b-vision-qlora.yml

@@ -1,64 +0,0 @@
-base_model: mistralai/Ministral-3-3B-Reasoning-2512
-processor_type: AutoProcessor
-
-# Enable to use mistral-common tokenizer
-tokenizer_use_mistral_common: true
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_4bit: true
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-# sample dataset below requires downloading image in advance
-# wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
-datasets:
-  - path: Nanobit/text-vision-2k-test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-weight_decay: 0.0
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/olmo3/README.md

@@ -6,16 +6,24 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 ## Getting started
 
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-3. Run the finetuning example:
+1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
 
+    Here is an example of how to install from pip:
     ```bash
-    axolotl train examples/olmo3/olmo3-7b-qlora.yaml
+    # Ensure you have a compatible version of Pytorch installed
+    pip3 install packaging setuptools wheel ninja
+    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+
+    # Install Cut Cross Entropy
+    python scripts/cutcrossentropy_install.py | sh
     ```
 
+2. Run the finetuning example:
+
+```bash
+axolotl train examples/olmo3/olmo3-7b-qlora.yaml
+```
+
 Let us know how it goes. Happy finetuning! 🚀
 
 ### TIPS

examples/qat_nvfp4/Gemma3-12B_baseline.yml

@@ -1,67 +0,0 @@
-base_model: google/gemma-3-12b-it
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-
-output_dir: ./outputs/out_gemma/
-
-sequence_len: 8096
-sample_packing: true
-flash_attention: true
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 4e-5
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Gemma3-12B_qat.yml

@@ -1,72 +0,0 @@
-base_model: google/gemma-3-12b-it
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-
-output_dir: ./outputs/qat_out_gemma/
-
-sequence_len: 8096
-sample_packing: true
-flash_attention: true
-
-qat:
-  activation_dtype: nvfp4
-  weight_dtype: nvfp4
-  group_size: 16 # only group_size of 16 is supported with nvfp4
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 4e-5
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Gemma3-12B_baseline.yml

@@ -1,67 +0,0 @@
-base_model: google/gemma-3-12b-it
-# Math finetuning configuration for Gemma3-12B
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/out_math_gemma/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 8
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 3e-5
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Gemma3-12B_qat.yml

@@ -1,72 +0,0 @@
-base_model: google/gemma-3-12b-it
-# Math finetuning configuration for Gemma3-12B
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/qat_out_math_gemma/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-qat:
-  activation_dtype: nvfp4
-  weight_dtype: nvfp4
-  group_size: 16 # only group_size of 16 is supported with nvfp4
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 8
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 3e-5
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Gemma3-27B_baseline.yml

@@ -1,68 +0,0 @@
-base_model: google/gemma-3-27b-it
-# Math finetuning configuration for Gemma3-27B
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/out_math_gemma27/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-6
-eta_min: 7e-7
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Gemma3-27B_qat.yml

@@ -1,73 +0,0 @@
-base_model: google/gemma-3-27b-it
-# Math finetuning configuration for Gemma3-27B
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/qat_out_math_gemma27/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-qat:
-  activation_dtype: nvfp4
-  weight_dtype: nvfp4
-  group_size: 16 # only group_size of 16 is supported with nvfp4
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-6
-eta_min: 7e-7
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Qwen2.5-72B_baseline.yml

@@ -1,67 +0,0 @@
-base_model: Qwen/Qwen2.5-72B
-# Math finetuning configuration for Qwen2.5-72B (non-instruct)
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-seed: 42
-chat_template: qwen_25
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/out_math_72b/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 8
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-6
-eta_min: 7e-7
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Qwen2.5-72B_qat.yml

@@ -1,72 +0,0 @@
-base_model: Qwen/Qwen2.5-72B
-# Math finetuning configuration for Qwen2.5-72B (non-instruct)
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-seed: 42
-chat_template: qwen_25
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/qat_out_math_72b/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-qat:
-  activation_dtype: nvfp4
-  weight_dtype: nvfp4
-  group_size: 16 # only group_size of 16 is supported with nvfp4
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 8
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-6
-eta_min: 7e-7
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Qwen2.5-72B_baseline.yml

@@ -1,67 +0,0 @@
-base_model: Qwen/Qwen2.5-72B
-# Alpaca finetuning configuration for Qwen2.5-72B
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-seed: 42
-chat_template: qwen_25
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-
-output_dir: ./outputs/out_qwen72b/
-
-sequence_len: 8096
-sample_packing: true
-flash_attention: true
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Qwen2.5-72B_qat.yml

@@ -1,72 +0,0 @@
-base_model: Qwen/Qwen2.5-72B
-# Alpaca finetuning configuration for Qwen2.5-72B
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
-seed: 42
-chat_template: qwen_25
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-
-output_dir: ./outputs/qat_out_qwen72b/
-
-sequence_len: 8096
-sample_packing: true
-flash_attention: true
-
-qat:
-  activation_dtype: nvfp4
-  weight_dtype: nvfp4
-  group_size: 16 # only group_size of 16 is supported with nvfp4
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qwen2/adamw-pretrain-fsdp2.yaml

@@ -1,70 +0,0 @@
-base_model: Qwen/Qwen2.5-0.5B
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-# Use random initialization for fair comparison
-reinit_weights: true
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-# Pretraining dataset
-pretraining_dataset:
-  - path: allenai/c4
-    name: en
-    type: pretrain
-    split: train
-
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./outputs/compare-adamw-pretrain
-
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project: dist_muon
-wandb_entity:
-wandb_watch:
-wandb_name: adamw
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 4
-num_epochs: 1
-max_steps: 305
-
-# AdamW optimizer settings (standard LR for AdamW)
-optimizer: adamw_torch_fused
-learning_rate: 0.0002
-weight_decay: 0.01
-lr_scheduler: cosine
-
-train_on_inputs: true
-group_by_length: false
-bf16: auto
-fp16: false
-tf32: false
-
-gradient_checkpointing: false
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 0
-saves_per_epoch: 1
-
-# Reproducibility
-seed: 42
-
-fsdp_config:
-  fsdp_version: 2
-  fsdp_offload_params: false
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_cpu_ram_efficient_loading: false
-  fsdp_reshard_after_forward: true
-
-special_tokens:

examples/qwen2/muon-pretrain-fsdp2.yaml

@@ -1,70 +0,0 @@
-base_model: Qwen/Qwen2.5-0.5B
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-# Use random initialization for fair comparison
-reinit_weights: true
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-# Pretraining dataset
-pretraining_dataset:
-  - path: allenai/c4
-    name: en
-    type: pretrain
-    split: train
-
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./outputs/compare-muon-pretrain
-
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project: dist_muon
-wandb_entity:
-wandb_watch:
-wandb_name: muon
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 4
-num_epochs: 1
-max_steps: 305
-
-# Muon optimizer settings
-optimizer: muon
-learning_rate: 0.02
-weight_decay: 0.01
-lr_scheduler: cosine
-
-train_on_inputs: true
-group_by_length: false
-bf16: auto
-fp16: false
-tf32: false
-
-gradient_checkpointing: false
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 0
-saves_per_epoch: 1
-
-# Reproducibility
-seed: 42
-
-fsdp_config:
-  fsdp_version: 2
-  fsdp_offload_params: false
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_cpu_ram_efficient_loading: false
-  fsdp_reshard_after_forward: true
-
-special_tokens:

examples/qwen3/README.md

@@ -1,46 +0,0 @@
-# Finetune Qwen3 with Axolotl
-
-[Qwen3](https://huggingface.co/collections/Qwen/qwen3) are a family of open source models trained by Alibaba.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-3. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/qwen3/32b-qlora.yaml
-    ```
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### Chat template masking a few tokens off
-
-If you notice that the `chat_template` masking for assistant prompts are off by a few tokens, please ensure that you are adding the below to the yaml.
-
-```yaml
-chat_template: qwen3
-```
-
-### TIPS
-
-- For inference, please check the official model card as it depends on your reasoning mode.
-- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [Qwen3 Blog](https://qwenlm.github.io/blog/qwen3/)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/trinity/README.md

@@ -1,38 +0,0 @@
-# Finetune ArceeAI's Trinity with Axolotl
-
-[Trinity](https://huggingface.co/collections/arcee-ai/trinity) is a family of open weight MoE models trained by Arcee.ai.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## Getting started
-
-1. Install Axolotl following the main from the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
-
-2. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/trinity/trinity-nano-preview-qlora.yaml
-    ```
-
-This config uses about 24.9 GiB VRAM.
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### TIPS
-
-- For inference, the official Arcee.ai team recommends `top_p: 0.75`, `temperature: 0.15`, `top_k: 50`, and `min_p: 0.06`.
-- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [Trinity Blog](https://www.arcee.ai/blog/the-trinity-manifesto)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/trinity/trinity-nano-preview-qlora.yaml

@@ -1,67 +0,0 @@
-base_model: arcee-ai/Trinity-Nano-Preview
-trust_remote_code: true
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-# CCE - N/A as of now
-# plugins:
-#   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-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
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-# flash_attention: true  # Not supported
-sdp_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

requirements.txt

@@ -5,7 +5,7 @@ bitsandbytes==0.48.2
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
-liger-kernel==0.6.4
+liger-kernel==0.6.3
 # END section
 
 packaging==23.2
@@ -20,16 +20,15 @@ deepspeed>=0.17.0
 trl==0.25.0
 hf_xet==1.2.0
 kernels>=0.9.0
-trackio>=0.13.0
-typing_extensions>=4.14.0
+trackio
 
 optimum==1.16.2
 hf_transfer
 sentencepiece
-gradio>=6.2.0,<7.0
+gradio==5.49.1
 
 modal==1.0.2
-pydantic>=2.10.6,<2.12
+pydantic>=2.10.6
 addict
 fire
 PyYAML>=6.0
@@ -68,8 +67,9 @@ openenv-core==0.1.0
 schedulefree==1.4.1
 
 axolotl-contribs-lgpl==0.0.7
-axolotl-contribs-mit==0.0.6
+axolotl-contribs-mit==0.0.5
+
 # telemetry
 posthog==6.7.11
 
-mistral-common==1.8.6
+mistral-common==1.8.5

scripts/cutcrossentropy_install.py

@@ -29,5 +29,5 @@ UV_PREFIX = "uv " if USE_UV else ""
 
 print(
     UNINSTALL_PREFIX
-    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f643b88"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@5eff953"'
 )

setup.py

@@ -66,6 +66,7 @@ def parse_requirements(extras_require_map):
                 extras_require_map.pop("fbgemm-gpu")
                 extras_require_map["fbgemm-gpu"] = ["fbgemm-gpu-genai==1.4.1"]
                 extras_require_map["vllm"] = ["vllm==0.11.1"]
+                _install_requires.pop(_install_requires.index(xformers_version))
             elif (major, minor) >= (2, 8):
                 extras_require_map.pop("fbgemm-gpu")
                 extras_require_map["fbgemm-gpu"] = ["fbgemm-gpu-genai==1.3.0"]

src/axolotl/cli/config.py

@@ -26,7 +26,6 @@ from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.tee import prepare_debug_log
-from axolotl.utils.trackio_ import setup_trackio_env_vars
 from axolotl.utils.trainer import prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
@@ -228,7 +227,6 @@ def load_cfg(
         cfg,
         capabilities={
             "bf16": is_torch_bf16_gpu_available(),
-            "fp8": compute_supports_fp8(),
             "n_gpu": int(os.environ.get("WORLD_SIZE", 1)),
             "compute_capability": gpu_version,
         },
@@ -247,7 +245,6 @@ def load_cfg(
     setup_wandb_env_vars(cfg)
     setup_mlflow_env_vars(cfg)
     setup_comet_env_vars(cfg)
-    setup_trackio_env_vars(cfg)
     plugin_set_cfg(cfg)
 
     TELEMETRY_MANAGER.send_event(event_type="config-processed", properties=cfg)
@@ -262,11 +259,3 @@ def load_cfg(
     )
 
     return cfg
-
-
-def compute_supports_fp8() -> bool:
-    try:
-        compute_capability = torch.cuda.get_device_capability()
-        return compute_capability >= (9, 0)
-    except RuntimeError:
-        return False

src/axolotl/cli/inference.py

@@ -288,8 +288,8 @@ def do_inference_gradio(
         title=cfg.get("gradio_title", "Axolotl Gradio Interface"),
     )
 
-    demo.launch(
-        footer_links=["gradio", "settings"],
+    demo.queue().launch(
+        show_api=False,
         share=cfg.get("gradio_share", True),
         server_name=cfg.get("gradio_server_name", "127.0.0.1"),
         server_port=cfg.get("gradio_server_port", None),

src/axolotl/cli/main.py

@@ -26,7 +26,7 @@ from axolotl.cli.utils import (
     launch_training,
 )
 from axolotl.integrations.lm_eval.cli import lm_eval
-from axolotl.utils import set_misc_env, set_pytorch_cuda_alloc_conf
+from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.config import AxolotlInputConfig
 
@@ -45,7 +45,6 @@ def cli():
     print_axolotl_text_art()
     load_dotenv()
     set_pytorch_cuda_alloc_conf()
-    set_misc_env()
 
 
 @cli.command()

src/axolotl/cli/quantize.py

@@ -8,7 +8,7 @@ from typing import Union
 from transformers import AutoConfig, AutoModelForCausalLM, TorchAoConfig
 
 from axolotl.cli.config import load_cfg
-from axolotl.loaders import load_processor, load_tokenizer
+from axolotl.loaders import load_tokenizer
 from axolotl.utils.logging import get_logger
 from axolotl.utils.quantization import (
     TorchAOQuantDType,
@@ -66,11 +66,6 @@ def do_quantize(
 
     LOG.info(f"Loading model from {model_path}.")
     tokenizer = load_tokenizer(cfg)
-
-    processor = None
-    if cfg.is_multimodal:
-        processor = load_processor(cfg, tokenizer)
-
     config = AutoConfig.from_pretrained(model_path)
     torch_dtype = config.torch_dtype if hasattr(config, "torch_dtype") else None
     model = AutoModelForCausalLM.from_pretrained(
@@ -112,10 +107,6 @@ def do_quantize(
         save_jinja_files=cfg.tokenizer_save_jinja_files,
     )
 
-    if processor:
-        LOG.info(f"Saving processor to: {str(Path(output_dir) / 'quantized')}.")
-        processor.save_pretrained(str(Path(output_dir) / "quantized"))
-
     if hub_model_id:
         hub_model_id = (
             hub_model_id.rstrip("-")
@@ -123,8 +114,6 @@ def do_quantize(
         )
         model.push_to_hub(hub_model_id, safe_serialization=False)
         tokenizer.push_to_hub(hub_model_id)
-        if processor:
-            processor.push_to_hub(hub_model_id)
         LOG.info(f"Quantized model pushed to: {hub_model_id}.")
 
     LOG.info(f"Quantized model saved to: {str(Path(output_dir) / 'quantized')}.")

src/axolotl/cli/utils/diffusion.py

@@ -366,8 +366,8 @@ def launch_diffusion_gradio_ui(
             outputs=[masked_preview, html_out],
         )
 
-        demo.launch(
-            footer_links=["gradio", "settings"],
+        demo.queue().launch(
+            show_api=False,
             share=cfg.get("gradio_share", True),
             server_name=cfg.get("gradio_server_name", "127.0.0.1"),
             server_port=cfg.get("gradio_server_port", None),

src/axolotl/common/architectures.py

@@ -14,9 +14,7 @@ MOE_ARCH_BLOCK = {
     "qwen3_moe": "Qwen3MoeSparseMoeBlock",
     "qwen3_vl_moe": "Qwen3VLMoeTextSparseMoeBlock",
     "deepseek_v2": "DeepseekV2MoE",
-    "glm4_moe": "Glm4MoeMoE",
     "deepseek_v3": "DeepseekV3MoE",
     "gpt_oss": "GptOssDecoderLayer",
     "lfm2_moe": "Lfm2MoeSparseMoeBlock",
-    "afmoe": "AfmoeMoE",
 }

src/axolotl/core/builders/base.py

@@ -35,7 +35,6 @@ from axolotl.utils import (
     is_comet_available,
     is_mlflow_available,
     is_opentelemetry_available,
-    is_trackio_available,
 )
 from axolotl.utils.callbacks import (
     GCCallback,
@@ -148,14 +147,6 @@ class TrainerBuilderBase(abc.ABC):
             callbacks.append(
                 SaveAxolotlConfigtoCometCallback(self.cfg.axolotl_config_path)
             )
-        if self.cfg.use_trackio and is_trackio_available():
-            from axolotl.utils.callbacks.trackio_ import (
-                SaveAxolotlConfigtoTrackioCallback,
-            )
-
-            callbacks.append(
-                SaveAxolotlConfigtoTrackioCallback(self.cfg.axolotl_config_path)
-            )
         if self.cfg.use_otel_metrics and is_opentelemetry_available():
             from axolotl.utils.callbacks.opentelemetry import (
                 OpenTelemetryMetricsCallback,
@@ -290,22 +281,11 @@ class TrainerBuilderBase(abc.ABC):
                 adam_kwargs["eps"] = training_args_kwargs.get("adam_epsilon")
 
             if self.cfg.optimizer == "muon":
-                _, device_mesh = build_parallelism_config(self.cfg)
-
-                if device_mesh is not None:
-                    from axolotl.contribs.mit.muon.dist_muon import (
-                        DistMuonOptimizerFactory,
-                    )
-
-                    optimizer_cls = DistMuonOptimizerFactory
-                    optimizer_kwargs["device_mesh"] = device_mesh
-                else:
-                    from axolotl.contribs.mit.muon import (
-                        MuonOptimizerFactory,
-                    )
-
-                    optimizer_cls = MuonOptimizerFactory
+                from axolotl.contribs.mit.muon import (
+                    MuonOptimizerFactory,
+                )
 
+                optimizer_cls = MuonOptimizerFactory
                 optimizer_kwargs.update(adam_kwargs)
             elif self.cfg.optimizer == "dion":
                 from axolotl.contribs.mit.dion import (
@@ -443,8 +423,6 @@ class TrainerBuilderBase(abc.ABC):
             report_to.append("tensorboard")
         if self.cfg.use_comet:
             report_to.append("comet_ml")
-        if self.cfg.use_trackio:
-            report_to.append("trackio")
 
         training_args_kwargs["report_to"] = report_to
 
@@ -452,8 +430,6 @@ class TrainerBuilderBase(abc.ABC):
             training_args_kwargs["run_name"] = self.cfg.wandb_name
         elif self.cfg.use_mlflow:
             training_args_kwargs["run_name"] = self.cfg.mlflow_run_name
-        elif self.cfg.use_trackio:
-            training_args_kwargs["run_name"] = self.cfg.trackio_run_name
         else:
             training_args_kwargs["run_name"] = None
 

src/axolotl/core/trainers/base.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import math
 import os
 from collections import defaultdict
 from functools import partial, wraps
@@ -604,7 +603,6 @@ class AxolotlTrainer(
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
-        metric_ndigits = int(os.getenv("AXOLOTL_METRIC_NDIGITS", "5"))
 
         for key, metric_data in self._stored_metrics[train_eval].items():
             values = torch.tensor(metric_data["values"])  # type: ignore[arg-type]
@@ -615,18 +613,7 @@ class AxolotlTrainer(
                 raise NotImplementedError(
                     "Metric reduction must be one of [mean, min, max, sum]"
                 )
-            logs[key] = round(fn(values).item(), metric_ndigits)
-
-        if "loss" in logs:
-            try:
-                logs["ppl"] = round(math.exp(logs["loss"]), metric_ndigits)
-            except OverflowError:
-                logs["ppl"] = float("inf")
-        if "eval_loss" in logs:
-            try:
-                logs["eval_ppl"] = round(math.exp(logs["eval_loss"]), metric_ndigits)
-            except OverflowError:
-                logs["eval_ppl"] = float("inf")
+            logs[key] = round(fn(values).item(), 4)
 
         if is_main_process():
             # Add memory usage
@@ -644,10 +631,8 @@ class AxolotlTrainer(
             logs["tokens_per_second_per_gpu"] = round(
                 self.state.last_tokens_per_second.item() / self.args.logging_steps, 2
             )
-            if (
-                hasattr(self.state, "total_tokens")
-                and self.state.total_tokens is not None
-            ):
+
+            if hasattr(self.state, "total_tokens"):
                 logs["total_tokens"] = int(self.state.total_tokens.item())
 
         del self._stored_metrics[train_eval]

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

@@ -36,6 +36,4 @@ class DPOStrategy:
             training_args_kwargs["dpo_norm_loss"] = cfg.dpo_norm_loss
         if cfg.dpo_use_logits_to_keep is not None:
             training_args_kwargs["use_logits_to_keep"] = cfg.dpo_use_logits_to_keep
-        if cfg.dpo_use_liger_kernel is not None:
-            training_args_kwargs["use_liger_kernel"] = cfg.dpo_use_liger_kernel
         return training_args_kwargs

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -19,7 +19,7 @@ python scripts/cutcrossentropy_install.py | sh
 
 - If you are installing from pip
 ```bash
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f643b88"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@5eff953"
 ```
 
 ## Usage
@@ -44,7 +44,6 @@ plugins:
 - gemma3n_text
 - glm
 - glm4
-- glm_moe
 - glm4_moe
 - glm4v
 - glm4v_moe
@@ -62,8 +61,6 @@ plugins:
 - llama4
 - llama4_text
 - llava
-- ministral
-- ministral3
 - mistral
 - mistral3
 - mixtral

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -35,7 +35,7 @@ LOG = get_logger(__name__)
 
 _CCE_INSTALL_MESSAGE = (
     "Please install Axolotl's fork of cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f643b88"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@5eff953"`'
 )
 
 

src/axolotl/integrations/densemixer/plugin.py

@@ -21,7 +21,7 @@ class DenseMixerPlugin(BasePlugin):
         if cfg.dense_mixer:
             if not importlib.util.find_spec("densemixer"):
                 raise RuntimeError(
-                    "DenseMixer is not installed. Install it with `pip install densemixer`"
+                    "DenseMixer is not installed. Install it with `pip install densemizer`"
                 )
 
             from densemixer.patching import (

src/axolotl/integrations/kd/chat_template.py

@@ -179,17 +179,8 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
         logprobs = prompt.pop(self.logprobs_field)
         tokenized_prompt = super()._tokenize_single_prompt(prompt)
         tokenized_prompt[self.logprobs_field] = logprobs
-
-        # let subclasses add fields before transform
-        tokenized_prompt = self._prepare_kd_fields(tokenized_prompt, prompt)
-
         tokenized_prompt = self.transform_logprobs(tokenized_prompt)
-        return tokenized_prompt
 
-    def _prepare_kd_fields(self, tokenized_prompt, original_prompt):
-        """
-        Hook for subclasses to prepare additional KD fields before transform
-        """
         return tokenized_prompt
 
 
@@ -292,13 +283,14 @@ class ChatTemplateStrategyWithKDv2(ChatTemplateStrategyWithKD):
 
         return sample
 
-    def _prepare_kd_fields(self, tokenized_prompt, original_prompt):
-        """
-        Add pre-tokenized target_token_ids for v2 format
-        """
-        target_token_ids = original_prompt.pop("target_token_ids", None)
+    def _tokenize_single_prompt(self, prompt):
+        target_token_ids = prompt.get("target_token_ids", None)
+
+        tokenized_prompt = super()._tokenize_single_prompt(prompt)
+
         if target_token_ids is not None:
             tokenized_prompt["target_token_ids"] = target_token_ids
+
         return tokenized_prompt
 
 

src/axolotl/integrations/kd/trainer.py

@@ -16,8 +16,6 @@
 KD trainer
 """
 
-from typing_extensions import override
-
 from axolotl.core.trainers.base import AxolotlTrainer
 
 from .kernels.liger import LigerFusedLinearKLTopKLogprobLoss
@@ -62,7 +60,6 @@ class AxolotlKDTrainer(AxolotlTrainer):
             if columns_to_add:
                 self._signature_columns += columns_to_add
 
-    @override
     def compute_loss(
         self,
         model,
@@ -82,22 +79,10 @@ class AxolotlKDTrainer(AxolotlTrainer):
         ):
             del inputs["attention_mask"]
 
-        if num_items_in_batch is None and "labels" in inputs:
-            num_items_in_batch = (inputs["labels"] != -100).sum().item()
-
         if self.model_accepts_loss_kwargs:
             loss_kwargs = {}
             if num_items_in_batch is not None:
                 loss_kwargs["num_items_in_batch"] = num_items_in_batch
             inputs = {**inputs, **loss_kwargs}
-
         outputs = model(**inputs)
-
-        if isinstance(outputs, dict):
-            loss = outputs["loss"]
-        elif isinstance(outputs, tuple):
-            loss = outputs[0]
-        else:
-            loss = outputs.loss if hasattr(outputs, "loss") else outputs
-
-        return (loss, outputs) if return_outputs else loss
+        return outputs[0]

src/axolotl/loaders/adapter.py

@@ -142,12 +142,9 @@ def load_lora(
     ):
         setup_quantized_meta_for_peft(model)
 
-    model_kwargs: Any = {}
-    if cfg.peft_autocast_adapter_dtype is not None:
-        model_kwargs["autocast_adapter_dtype"] = cfg.peft_autocast_adapter_dtype
-
     if cfg.lora_model_dir:
         LOG.debug("Loading pretrained PEFT - LoRA")
+        model_kwargs: Any = {}
         if cfg.lora_on_cpu:
             model_kwargs["max_memory"] = {"cpu": "256GiB"}
             model_kwargs["device_map"] = {"": "cpu"}
@@ -158,7 +155,7 @@ def load_lora(
             **model_kwargs,
         )
     else:
-        model = get_peft_model(model, lora_config, **model_kwargs)
+        model = get_peft_model(model, lora_config)
 
     if rank == 0:
         try:

src/axolotl/monkeypatch/multipack.py

@@ -37,7 +37,6 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "deepseek_v3",
     "glm",
     "glm4",
-    "glm4_moe",
     "smollm3",
     "granite",
     "granitemoe",
@@ -53,9 +52,6 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "olmo",
     "olmo2",
     "olmo3",
-    "ministral",
-    "ministral3",
-    "afmoe",
 ]
 
 

src/axolotl/prompt_strategies/chat_template.py

@@ -95,7 +95,6 @@ class ChatTemplatePrompter(Prompter):
         add_generation_prompt=False,
         images=None,
         tools=None,
-        real_last_index=None,
     ):
         """
         Build a prompt from a conversation.
@@ -115,9 +114,6 @@ class ChatTemplatePrompter(Prompter):
         if tools:
             chat_template_kwargs["tools"] = tools
 
-        if real_last_index:
-            chat_template_kwargs["real_last_index"] = real_last_index
-
         if self.processor:
             if not callable(self.processor):
                 raise TypeError("Processor must be callable")
@@ -635,17 +631,11 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         turns_with_empty = turns[:turn_idx] + [empty_turn]
         turns_with_content = turns[: turn_idx + 1]
 
-        real_last_index = len(turns) - 1
-
         # Generate the conversation up to the turn, with final turn replaced with dummy content
-        dummy_ids = self.prompter.build_prompt(
-            turns_with_empty, tools=tools, real_last_index=real_last_index
-        )  # type: ignore
+        dummy_ids = self.prompter.build_prompt(turns_with_empty, tools=tools)  # type: ignore
 
         # Generate the conversation up to the turn, with final turn included
-        full_ids = self.prompter.build_prompt(
-            turns_with_content, tools=tools, real_last_index=real_last_index
-        )  # type: ignore
+        full_ids = self.prompter.build_prompt(turns_with_content, tools=tools)  # type: ignore
 
         if not full_ids or not dummy_ids:
             LOG.warning(f"Empty template generated for turn {turn_idx}")

src/axolotl/utils/__init__.py

@@ -24,10 +24,6 @@ def is_opentelemetry_available():
     )
 
 
-def is_trackio_available():
-    return importlib.util.find_spec("trackio") is not None
-
-
 def get_pytorch_version() -> tuple[int, int, int]:
     """
     Get Pytorch version as a tuple of (major, minor, patch).
@@ -45,27 +41,14 @@ def get_pytorch_version() -> tuple[int, int, int]:
 
 
 def set_pytorch_cuda_alloc_conf():
-    """Set up CUDA allocation config"""
+    """Set up CUDA allocation config if using PyTorch >= 2.2"""
     torch_version = torch.__version__.split(".")
     torch_major, torch_minor = int(torch_version[0]), int(torch_version[1])
-    config_value = "expandable_segments:True,roundup_power2_divisions:16"
-    if (
-        torch_major == 2
-        and torch_minor >= 9
-        and os.getenv("PYTORCH_ALLOC_CONF") is None
-    ):
-        os.environ["PYTORCH_ALLOC_CONF"] = config_value
-    elif (
-        torch_major == 2
-        and torch_minor >= 2
-        and os.getenv("PYTORCH_CUDA_ALLOC_CONF") is None
-    ):
-        os.environ["PYTORCH_CUDA_ALLOC_CONF"] = config_value
-
-
-def set_misc_env():
-    if os.getenv("XFORMERS_IGNORE_FLASH_VERSION_CHECK") is None:
-        os.environ["XFORMERS_IGNORE_FLASH_VERSION_CHECK"] = "1"
+    if torch_major == 2 and torch_minor >= 2:
+        if os.getenv("PYTORCH_CUDA_ALLOC_CONF") is None:
+            os.environ["PYTORCH_CUDA_ALLOC_CONF"] = (
+                "expandable_segments:True,roundup_power2_divisions:16"
+            )
 
 
 def get_not_null(value, default=None):

src/axolotl/utils/callbacks/trackio_.py

@@ -1,44 +0,0 @@
-"""Trackio module for trainer callbacks"""
-
-from typing import TYPE_CHECKING
-
-import trackio
-from transformers import TrainerCallback, TrainerControl, TrainerState
-
-from axolotl.utils.distributed import is_main_process
-from axolotl.utils.environment import is_package_version_ge
-from axolotl.utils.logging import get_logger
-
-if TYPE_CHECKING:
-    from axolotl.core.training_args import AxolotlTrainingArguments
-
-LOG = get_logger(__name__)
-
-
-class SaveAxolotlConfigtoTrackioCallback(TrainerCallback):
-    """Callback for trackio integration"""
-
-    def __init__(self, axolotl_config_path):
-        self.axolotl_config_path = axolotl_config_path
-
-    def on_train_begin(
-        self,
-        args: "AxolotlTrainingArguments",
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        if is_main_process():
-            try:
-                if not is_package_version_ge("trackio", "0.11.0"):
-                    LOG.warning(
-                        "Trackio version 0.11.0 or higher is required to save config files. "
-                        "Please upgrade trackio: pip install --upgrade trackio"
-                    )
-                    return control
-
-                trackio.save(self.axolotl_config_path)
-                LOG.info("The Axolotl config has been saved to Trackio.")
-            except (FileNotFoundError, ConnectionError, AttributeError) as err:
-                LOG.warning(f"Error while saving Axolotl config to Trackio: {err}")
-        return control

src/axolotl/utils/chat_templates/templates/qwen3.jinja

@@ -15,12 +15,6 @@
     {%- endif %}
 {%- endif %}
 {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
-{#- Determine the real last index: use provided value or default to messages length - 1 #}
-{%- if real_last_index is defined and real_last_index is not none %}
-    {%- set ns.real_last_index = real_last_index %}
-{%- else %}
-    {%- set ns.real_last_index = messages|length - 1 %}
-{%- endif %}
 {%- for message in messages[::-1] %}
     {%- set index = (messages|length - 1) - loop.index0 %}
     {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
@@ -43,7 +37,7 @@
             {%- endif %}
         {%- endif %}
         {%- if loop.index0 > ns.last_query_index %}
-            {%- if loop.index0 == ns.real_last_index or (loop.index0 != ns.real_last_index and reasoning_content) %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
                 {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
             {%- else %}
                 {{- '<|im_start|>' + message.role + '\n' + content }}

src/axolotl/utils/data/streaming.py

@@ -203,7 +203,6 @@ def wrap_streaming_dataset(
             max_seq_length=cfg.sequence_len,
             batch_size=cfg.micro_batch_size,
             multipack_attn=multipack_attn,
-            bin_size=cfg.sample_packing_bin_size,
         )
 
         # Set this to 1 so downstream data_loader doesn't try to increase the batch size
@@ -255,7 +254,6 @@ def encode_packed_streaming(
     collate_fn,
     ds_wrapper: Callable,
     examples: Dict[str, List],
-    bin_size: int,
     max_seq_length: int = 2048,
     batch_size: int = 4,
     multipack_attn: Optional[bool] = True,
@@ -280,7 +278,6 @@ def encode_packed_streaming(
         batch_max_len=batch_size * max_seq_length,
         drop_last=True,
         num_processes=1,
-        bin_size=bin_size,
     )
 
     chunked_data = defaultdict(list)

src/axolotl/utils/data/utils.py

@@ -188,10 +188,7 @@ def handle_long_seq_in_dataset(
         cfg: Dictionary mapping `axolotl` config keys to values.
 
     Returns:
-        Filtered dataset with long sequences handled according to the excess_length_strategy value:
-            'drop' (default)    excludes any sequence longer than sequence_len
-            'truncate'          truncates them down to sequence_len
-            'raise'             raises a ValueError if any sequence was found that was longer than sequence_len
+        Filtered dataset with long sequences removed.
     """
     if (
         hasattr(dataset, "column_names")
@@ -209,13 +206,10 @@ def handle_long_seq_in_dataset(
         )
         return dataset
 
-    excess_length_strategy = (cfg.excess_length_strategy or "drop").lower()
-
     drop_long = functools.partial(
         drop_long_seq,
         sequence_len=sequence_len,
         min_sequence_len=cfg.min_sample_len,
-        raise_on_drop=excess_length_strategy == "raise",
     )
 
     with contextlib.suppress(AttributeError):
@@ -234,13 +228,9 @@ def handle_long_seq_in_dataset(
 
     drop_long_kwargs = {}
     if filter_map_kwargs:
-        action = (
-            "Checking Sequence Lengths"
-            if excess_length_strategy == "raise"
-            else "Dropping Long Sequences"
-        )
-        drop_long_kwargs["desc"] = f"{action} (>{sequence_len})"
+        drop_long_kwargs["desc"] = f"Dropping Long Sequences (>{sequence_len})"
 
+    excess_length_strategy = (cfg.excess_length_strategy or "drop").lower()
     if excess_length_strategy == "truncate":
         process_fn = functools.partial(
             truncate_long_seq,

src/axolotl/utils/mistral/mistral_tokenizer.py

@@ -80,9 +80,6 @@ class HFMistralTokenizer(MistralCommonTokenizer):
     ) -> str | list[int]:
         """Patched fn to handle setting serving mode, continue_final_message, remove chat_template and add_generation_prompt kwarg"""
 
-        # pop unnecessary kwarg for mistral
-        kwargs.pop("real_last_index", None)
-
         try:
             if add_generation_prompt:
                 self._set_mode(ValidationMode.serving)
@@ -221,10 +218,3 @@ class HFMistralTokenizer(MistralCommonTokenizer):
             model_input_names=model_input_names,
             clean_up_tokenization_spaces=clean_up_tokenization_spaces,
         )
-
-    def save_pretrained(self, *args, **kwargs) -> tuple[str, ...]:
-        """
-        Patches to remove save_jinja_files from being passed onwards.
-        """
-        kwargs.pop("save_jinja_files", None)
-        return super().save_pretrained(*args, **kwargs)

src/axolotl/utils/samplers/multipack.py

@@ -260,12 +260,12 @@ class MultipackBatchSampler(BatchSampler):
         batch_size: int,  # Number of bins per batch
         batch_max_len: int,  # Maximum sequence length (bin capacity)
         lengths: np.ndarray,  # Sequence lengths
-        bin_size: int,  # The max number of samples that can be packed in a single bin
         packing_efficiency_estimate: float = 1.0,  # Initial efficiency estimate
         drop_last: bool = True,  # Whether to drop final batches (might be incomplete)
         num_count_samples: int = 4,  # Number of times to estimate batch count
         sequential: bool = False,  # Whether to use sequential packing
         group_size: int = 100_000,  # Size of groups for parallel packing
+        bin_size: int = 200,  # The max number of samples that can be packed in a single bin
         num_processes: int | None = None,  # Number of processes for parallel packing
         safe_mode: bool = True,  # Conservative packing to prevent training instability
         mp_start_method: str = "fork",
@@ -343,7 +343,7 @@ class MultipackBatchSampler(BatchSampler):
                 lengths,
                 bin_capacity=self.batch_max_len,
                 group_size=self.group_size,
-                bin_size=self.bin_size or self.batch_max_len,
+                bin_size=self.bin_size,
                 num_processes=min(4, num_processes) if num_processes else 4,
                 safe_mode=self.safe_mode,
                 mp_start_method=self.mp_start_method,

src/axolotl/utils/schemas/config.py

@@ -2,7 +2,6 @@
 
 from typing import Annotated, Any, Literal
 
-from accelerate.utils import is_fp8_available
 from annotated_types import MinLen
 from packaging import version
 from pydantic import (
@@ -34,7 +33,6 @@ from axolotl.utils.schemas.integrations import (
     MLFlowConfig,
     OpenTelemetryConfig,
     RayConfig,
-    TrackioConfig,
     WandbConfig,
 )
 from axolotl.utils.schemas.internal import EnvCapabilities, GPUCapabilities
@@ -64,7 +62,6 @@ class AxolotlInputConfig(
     WandbConfig,
     MLFlowConfig,
     CometConfig,
-    TrackioConfig,
     OpenTelemetryConfig,
     LISAConfig,
     GradioConfig,
@@ -176,12 +173,6 @@ class AxolotlInputConfig(
     dpo_use_logits_to_keep: bool | None = None
     dpo_label_smoothing: float | None = None
     dpo_norm_loss: bool | None = None
-
-    dpo_use_liger_kernel: bool | None = Field(
-        default=None,
-        json_schema_extra={"description": "Whether to use Liger kernel for DPO loss."},
-    )
-
     dpo_padding_free: bool | None = None
     dpo_generate_during_eval: bool | None = None
 
@@ -454,10 +445,10 @@ class AxolotlInputConfig(
             "description": "The maximum length of an input to train with, this should typically be less than 2048 as most models have a token/context limit of 2048"
         },
     )
-    excess_length_strategy: Literal["drop", "truncate", "raise"] | None = Field(
+    excess_length_strategy: Literal["drop", "truncate"] | None = Field(
         default=None,
         json_schema_extra={
-            "description": "What to do when a tokenized row exceeds sequence_len. 'drop' removes the row; 'truncate' slices tensors to sequence_len; 'raise' raises a ValueError. Defaults to 'drop' for backward compatibility."
+            "description": "What to do when a tokenized row exceeds sequence_len. 'drop' removes the row; 'truncate' slices tensors to sequence_len. Defaults to 'drop' for backward compatibility."
         },
     )
     eval_sequence_len: int | None = Field(
@@ -1101,16 +1092,6 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
                 )
         return self
 
-    @model_validator(mode="after")
-    def check_fp8(self):
-        if self.fp8 and not self.capabilities.fp8:
-            raise ValueError("fp8 requested, but fp8 is not supported on this GPU")
-        elif self.fp8 and self.capabilities.fp8 and not is_fp8_available():
-            raise ValueError(
-                "fp8 requested, but missing one of ms-amp, transformers-engine or torchao."
-            )
-        return self
-
     @model_validator(mode="before")
     @classmethod
     def check_sample_packing_w_sdpa_bf16(cls, data):

src/axolotl/utils/schemas/integrations.py

@@ -200,23 +200,3 @@ class OpenTelemetryConfig(BaseModel):
             "description": "Port for the Prometheus metrics HTTP server"
         },
     )
-
-
-class TrackioConfig(BaseModel):
-    """Trackio configuration subset"""
-
-    use_trackio: bool | None = None
-    trackio_project_name: str | None = Field(
-        default=None,
-        json_schema_extra={"description": "Your trackio project name"},
-    )
-    trackio_run_name: str | None = Field(
-        default=None,
-        json_schema_extra={"description": "Set the name of your trackio run"},
-    )
-    trackio_space_id: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Hugging Face Space ID to sync dashboard to (optional, runs locally if not provided)"
-        },
-    )

src/axolotl/utils/schemas/peft.py

@@ -109,12 +109,6 @@ class LoraConfig(BaseModel):
             )
         },
     )
-    peft_autocast_adapter_dtype: bool | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Whether to upcast the LoRA adapter to fp32. This is enabled by default in PEFT."
-        },
-    )
 
     qlora_sharded_model_loading: bool | None = Field(
         default=False,

src/axolotl/utils/schemas/validation.py

@@ -751,19 +751,12 @@ class OptimizationValidationMixin:
     @model_validator(mode="before")
     @classmethod
     def check_muon_deepspeed_fsdp(cls, data):
-        if data.get("optimizer") == "muon":
-            if data.get("deepspeed"):
-                raise ValueError(
-                    "Muon optimizer is currently incompatible with DeepSpeed"
-                )
-            if data.get("fsdp") or data.get("fsdp_config"):
-                fsdp_version = data.get("fsdp_version")
-                if fsdp_version is None:
-                    fsdp_version = data.get("fsdp_config", {}).get("fsdp_version", 1)
-                if str(fsdp_version) != "2":
-                    raise ValueError(
-                        "Muon optimizer is only compatible with FSDP2. Set fsdp_version: 2 to use Muon with FSDP."
-                    )
+        if data.get("optimizer") == "muon" and (
+            data.get("deepspeed") or data.get("fsdp") or data.get("fsdp_config")
+        ):
+            raise ValueError(
+                "Muon optimizer is currently incompatible with DeepSpeed and FSDP"
+            )
         return data
 
     @model_validator(mode="before")
@@ -847,6 +840,40 @@ class OptimizationValidationMixin:
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_fsdp_version_in_fsdp_config(cls, data):
+        fsdp_config = data.get("fsdp_config") or {}
+        if fsdp_config and fsdp_config.get("fsdp_version"):
+            LOG.warning(
+                "Configuring `fsdp_version` in `fsdp_config` is deprecated. "
+                "Please configure `fsdp_version` as a top-level field."
+            )
+            data["fsdp_version"] = fsdp_config.pop("fsdp_version")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_fsdp_config_kwargs_prefix(cls, data):
+        if fsdp_config := data.get("fsdp_config"):
+            should_fix = False
+            for key, _ in fsdp_config.items():
+                if key.startswith("fsdp_"):
+                    should_fix = True
+                    LOG.warning_once(
+                        "Configuring FSDP fields with the `fsdp_` prefix is deprecated. "
+                        "Please omit the `fsdp_` prefix from the any fields in `fsdp_config`."
+                    )
+            if should_fix:
+                update_fsdp_config = {}
+                for key, value in fsdp_config.items():
+                    if key.startswith("fsdp_") and key != "fsdp_version":
+                        update_fsdp_config[key.replace("fsdp_", "")] = value
+                    else:
+                        update_fsdp_config[key] = value
+                data["fsdp_config"] = update_fsdp_config
+        return data
+
     @model_validator(mode="after")
     def check_fsdp_offload_w_8bit_optimizer(self):
         if (
@@ -948,40 +975,6 @@ class OptimizationValidationMixin:
 
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_fsdp_version_in_fsdp_config(cls, data):
-        fsdp_config = data.get("fsdp_config") or {}
-        if fsdp_config and fsdp_config.get("fsdp_version"):
-            LOG.warning(
-                "Configuring `fsdp_version` in `fsdp_config` is deprecated. "
-                "Please configure `fsdp_version` as a top-level field."
-            )
-            data["fsdp_version"] = fsdp_config.pop("fsdp_version")
-        return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_fsdp_config_kwargs_prefix(cls, data):
-        if fsdp_config := data.get("fsdp_config"):
-            should_fix = False
-            for key, _ in fsdp_config.items():
-                if key.startswith("fsdp_"):
-                    should_fix = True
-                    LOG.warning_once(
-                        "Configuring FSDP fields with the `fsdp_` prefix is deprecated. "
-                        "Please omit the `fsdp_` prefix from the any fields in `fsdp_config`."
-                    )
-            if should_fix:
-                update_fsdp_config = {}
-                for key, value in fsdp_config.items():
-                    if key.startswith("fsdp_") and key != "fsdp_version":
-                        update_fsdp_config[key.replace("fsdp_", "")] = value
-                    else:
-                        update_fsdp_config[key] = value
-                data["fsdp_config"] = update_fsdp_config
-        return data
-
 
 class SystemValidationMixin:
     """Validation methods related to system and hardware configuration."""

src/axolotl/utils/trackio_.py

@@ -1,17 +0,0 @@
-"""Module for trackio utilities"""
-
-import os
-
-from axolotl.utils.dict import DictDefault
-
-
-def setup_trackio_env_vars(cfg: DictDefault):
-    for key in cfg.keys():
-        if key.startswith("trackio_"):
-            value = cfg.get(key, "")
-
-            if value and isinstance(value, str) and len(value) > 0:
-                os.environ[key.upper()] = value
-
-    if cfg.trackio_project_name and len(cfg.trackio_project_name) > 0:
-        cfg.use_trackio = True

src/axolotl/utils/trainer.py

@@ -205,15 +205,12 @@ def add_length(sample):
     return sample
 
 
-def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2, raise_on_drop=False):
+def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):
     """
     Drop samples whose sequence length is either too long (> sequence_len)
     or too short (< min_sequence_len).
 
     Works for both single-example (list[int]) or batched (list[list[int]]).
-
-    If raise_on_drop is set, the code raises a ValueError if a sample is
-    encountered that is too long and would have been dropped.
     """
     min_sequence_len = min_sequence_len or 2
 
@@ -228,20 +225,12 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2, raise_on_drop=F
     if isinstance(input_ids[0], int):
         # Single example (input_ids is a list of int)
         length = len(input_ids)
-        if raise_on_drop and length > sequence_len:
-            raise ValueError(
-                f"Sequence encountered with {length} tokens, which exceeds the maximum {sequence_len}."
-            )
         return min_sequence_len <= length <= sequence_len
 
     # Batched (input_ids is a list of lists)
     results = []
     for seq in input_ids:
         length = len(seq)
-        if raise_on_drop and length > sequence_len:
-            raise ValueError(
-                f"Sequence encountered with {length} tokens, which exceeds the maximum {sequence_len}."
-            )
         results.append(min_sequence_len <= length <= sequence_len)
     return results
 

tests/core/test_builders.py

@@ -474,8 +474,10 @@ def rand_reward_func(prompts, completions) -> list[float]:
 
             assert trainer.optimizer_cls_and_kwargs is not None
 
-            from axolotl.contribs.mit.muon import MuonOptimizerFactory
-            from axolotl.contribs.mit.muon.muon import Muon
+            from axolotl.contribs.mit.muon import (
+                Muon,
+                MuonOptimizerFactory,
+            )
 
             optimizer_cls, optimizer_kwargs = trainer.optimizer_cls_and_kwargs
             assert optimizer_cls is MuonOptimizerFactory
@@ -554,8 +556,10 @@ class TestHFCausalTrainerBuilder:
 
         assert trainer.optimizer_cls_and_kwargs is not None
 
-        from axolotl.contribs.mit.muon import MuonOptimizerFactory
-        from axolotl.contribs.mit.muon.muon import Muon
+        from axolotl.contribs.mit.muon import (
+            Muon,
+            MuonOptimizerFactory,
+        )
 
         optimizer_cls, optimizer_kwargs = trainer.optimizer_cls_and_kwargs
         assert optimizer_cls is MuonOptimizerFactory

tests/e2e/multigpu/test_dist_muon_fsdp2.py

@@ -1,168 +0,0 @@
-"""Test module for DistMuon optimizer with FSDP2 multi-GPU functionality."""
-
-import os
-from pathlib import Path
-
-import torch
-import yaml
-from accelerate.test_utils import execute_subprocess_async
-from tbparse import SummaryReader
-from transformers.testing_utils import get_torch_dist_unique_port
-
-from axolotl.utils.dict import DictDefault
-
-from tests.e2e.utils import most_recent_subdir, require_torch_2_7_0
-
-AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
-
-
-def verify_training_success(temp_dir):
-    """Verify that training completed successfully by checking artifacts and loss."""
-    output_path = Path(temp_dir)
-
-    model_files = list(output_path.glob("*.bin")) + list(
-        output_path.glob("*.safetensors")
-    )
-    assert len(model_files) > 0, "No model files found - training may have failed"
-
-    checkpoint_files = list(output_path.glob("checkpoint-*"))
-    assert len(checkpoint_files) > 0, (
-        "No checkpoint files found - training may have failed"
-    )
-
-    tb_log_path = most_recent_subdir(temp_dir + "/runs")
-    if tb_log_path:
-        event_files = sorted(os.listdir(tb_log_path))
-        if event_files:
-            event_file = os.path.join(tb_log_path, event_files[0])
-            reader = SummaryReader(event_file)
-            df = reader.scalars
-            train_loss_df = df[df.tag == "train/train_loss"]
-            if len(train_loss_df) > 0:
-                final_loss = train_loss_df.value.values[-1]
-                assert not torch.isnan(torch.tensor(final_loss)), (
-                    f"Training loss is NaN: {final_loss}"
-                )
-
-
-class TestDistMuon:
-    """Test class for DistMuon optimizer with FSDP2 functionality."""
-
-    @require_torch_2_7_0
-    def test_fft_sft(self, temp_dir):
-        cfg = DictDefault(
-            {
-                "base_model": "Qwen/Qwen2.5-0.5B",
-                "sequence_len": 2048,
-                "val_set_size": 0.01,
-                "datasets": [
-                    {
-                        "path": "tatsu-lab/alpaca",
-                        "type": "alpaca",
-                        "split": "train[:10%]",
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 2,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.02,
-                "optimizer": "muon",
-                "weight_decay": 0.01,
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-                "fsdp_version": 2,
-                "fsdp_config": {
-                    "offload_params": False,
-                    "cpu_ram_efficient_loading": False,
-                    "transformer_layer_cls_to_wrap": "Qwen2DecoderLayer",
-                    "state_dict_type": "FULL_STATE_DICT",
-                    "auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
-                    "reshard_after_forward": True,
-                },
-                "use_tensorboard": True,
-                "bf16": 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(
-            [
-                "axolotl",
-                "train",
-                str(Path(temp_dir) / "config.yaml"),
-                "--num-processes",
-                "2",
-                "--main-process-port",
-                f"{get_torch_dist_unique_port()}",
-            ]
-        )
-
-        verify_training_success(temp_dir)
-
-    @require_torch_2_7_0
-    def test_lora_sft(self, temp_dir):
-        cfg = DictDefault(
-            {
-                "base_model": "Qwen/Qwen2.5-0.5B",
-                "sequence_len": 2048,
-                "val_set_size": 0.01,
-                "datasets": [
-                    {
-                        "path": "tatsu-lab/alpaca",
-                        "type": "alpaca",
-                        "split": "train[:10%]",
-                    },
-                ],
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "num_epochs": 1,
-                "max_steps": 2,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.02,
-                "optimizer": "muon",
-                "weight_decay": 0.01,
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-                "fsdp_version": 2,
-                "fsdp_config": {
-                    "offload_params": False,
-                    "cpu_ram_efficient_loading": False,
-                    "transformer_layer_cls_to_wrap": "Qwen2DecoderLayer",
-                    "state_dict_type": "FULL_STATE_DICT",
-                    "auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
-                    "reshard_after_forward": True,
-                },
-                "use_tensorboard": True,
-                "bf16": 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(
-            [
-                "axolotl",
-                "train",
-                str(Path(temp_dir) / "config.yaml"),
-                "--num-processes",
-                "2",
-                "--main-process-port",
-                f"{get_torch_dist_unique_port()}",
-            ]
-        )
-
-        verify_training_success(temp_dir)

tests/integrations/test_kd_chat_template.py

@@ -1,81 +0,0 @@
-"""
-Test for KD chat template strategies
-"""
-
-from unittest.mock import Mock
-
-import pytest
-
-from axolotl.integrations.kd.chat_template import ChatTemplateStrategyWithKDv2
-
-
-class TestChatTemplateStrategyWithKDv2:
-    """Test v2 strategy correctly handles target_token_ids"""
-
-    @pytest.fixture
-    def v2_strategy(self):
-        """Create v2 strategy instance with mocked dependencies"""
-        # Mock prompter
-        mock_prompter = Mock()
-        mock_prompter.roles = {"user": "user", "assistant": "assistant"}
-        mock_prompter.chat_template_msg_variables = ["role", "content"]
-        mock_prompter.chat_template = "{{ messages }}"
-
-        # Mock tokenizer
-        mock_tokenizer = Mock()
-        mock_tokenizer.pad_token_id = 0
-        mock_tokenizer.eos_token_id = 2
-        mock_tokenizer.bos_token_id = 1
-        mock_tokenizer.eos_token = "<|endoftext|>"
-        mock_tokenizer.apply_chat_template = Mock(return_value=[1, 10, 20, 30, 2])
-        mock_tokenizer.encode = Mock(return_value=[2])
-
-        return ChatTemplateStrategyWithKDv2(
-            prompter=mock_prompter,
-            tokenizer=mock_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            logprobs_field="logprobs",
-            gen_temperature=1.0,
-            kd_temperature=1.0,
-        )
-
-    def test_v2_prepare_kd_fields_adds_target_token_ids(self, v2_strategy):
-        """
-        Test that v2's _prepare_kd_fields hook adds target_token_ids.
-
-        Validates the Template Method pattern fix where v2 overrides
-        the hook to add target_token_ids before transform.
-        """
-        tokenized = {"input_ids": [1, 10, 20, 30, 2], "labels": [1, 10, 20, 30, 2]}
-        original = {"target_token_ids": [[10, 20], [30, 40]]}
-
-        result = v2_strategy._prepare_kd_fields(tokenized, original)
-
-        assert "target_token_ids" in result
-        assert result["target_token_ids"] == [[10, 20], [30, 40]]
-
-    def test_v2_prepare_kd_fields_handles_missing_field(self, v2_strategy):
-        """Test hook handles missing target_token_ids gracefully"""
-        tokenized = {"input_ids": [1, 10, 20, 30, 2], "labels": [1, 10, 20, 30, 2]}
-        original = {}
-
-        result = v2_strategy._prepare_kd_fields(tokenized, original)
-
-        assert "target_token_ids" not in result
-
-    def test_v2_transform_requires_target_token_ids(self, v2_strategy):
-        """
-        Test v2's transform fails without target_token_ids.
-
-        Validates the bug fix - transform expects target_token_ids
-        to be added by the hook.
-        """
-        sample = {
-            "input_ids": [1, 10, 20, 30, 2],
-            "labels": [1, 10, 20, 30, 2],
-            "logprobs": [[-0.1, -0.2], [-0.3, -0.4]],
-        }
-
-        with pytest.raises(KeyError, match="target_token_ids"):
-            v2_strategy.transform_logprobs(sample)

tests/test_data.py

@@ -7,7 +7,6 @@ import unittest
 from transformers import LlamaTokenizer
 
 from axolotl.utils.data import encode_streaming, md5
-from axolotl.utils.trainer import drop_long_seq
 
 from tests.hf_offline_utils import enable_hf_offline
 
@@ -64,42 +63,6 @@ class TestEncodePretraining(unittest.TestCase):
             md5("hello world", "utf-8"), "5eb63bbbe01eeed093cb22bb8f5acdc3"
         )
 
-    def test_excess_length_strategy(self):
-        """Test that excess_length_strategy results in a value error when set to 'raise'."""
-
-        # -- single sequence --
-        # This should work
-        data = {"input_ids": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}
-        drop_long_seq(data, 32, raise_on_drop=True)
-
-        # This should return True, since data fits
-        dropped = drop_long_seq(data, 32)
-        self.assertTrue(dropped)
-
-        # This should raise
-        self.assertRaises(ValueError, drop_long_seq, data, 15, raise_on_drop=True)
-
-        # This should return False, since data doesn't fit
-        dropped = drop_long_seq(data, 15)
-        self.assertFalse(dropped)
-
-        # -- batch sequence --
-        # This should work
-        data = {
-            "input_ids": [
-                [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
-                [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-            ]
-        }
-        drop_long_seq(data, 32, raise_on_drop=True)
-
-        # This should raise
-        self.assertRaises(ValueError, drop_long_seq, data, 15, raise_on_drop=True)
-
-        # This should keep the first but drop the second entry
-        dropped = drop_long_seq(data, 15)
-        self.assertEqual(dropped, [True, False])
-
 
 if __name__ == "__main__":
     unittest.main()

tests/test_datasets.py

@@ -13,9 +13,7 @@ from transformers import PreTrainedTokenizer
 
 from axolotl.loaders.tokenizer import load_tokenizer
 from axolotl.utils.data.rl import prepare_preference_datasets
-from axolotl.utils.data.sft import (
-    _load_tokenized_prepared_datasets,
-)
+from axolotl.utils.data.sft import _load_tokenized_prepared_datasets
 from axolotl.utils.dict import DictDefault
 
 from tests.constants import (

tests/test_validation_dataset.py

@@ -363,5 +363,5 @@ class TestOptimizerValidation(BaseValidation):
             }
         )
 
-        with pytest.raises(ValueError, match=r".*only compatible with FSDP2.*"):
+        with pytest.raises(ValueError, match=r".*is currently incompatible with*"):
             validate_config(cfg)

tests/utils/schemas/validation/test_fsdp.py

@@ -123,17 +123,6 @@ class TestFSDPValidation:
         assert cfg.fsdp_config.transformer_layer_cls_to_wrap == "LlamaDecoderLayer"
         assert cfg.fsdp_config.reshard_after_forward is True
 
-    def test_muon_fsdp1_rejected(self, min_base_cfg):
-        cfg = min_base_cfg | DictDefault(
-            optimizer="muon",
-            fsdp_version=1,
-            fsdp_config={"reshard_after_forward": True},
-        )
-        with pytest.raises(
-            ValueError, match="Muon optimizer is only compatible with FSDP2"
-        ):
-            validate_config(cfg)
-
     @pytest.mark.parametrize(
         "rl",
         [