strip only starting 'v' char; e.g don't strip from '.dev'

.github/workflows/base.yml

@@ -51,14 +51,6 @@ jobs:
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "129"
-            cuda_version: 12.9.1
-            cudnn_version: ""
-            python_version: "3.12"
-            pytorch: 2.9.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "130"
             cuda_version: 13.0.0
             cudnn_version: ""
@@ -67,14 +59,6 @@ jobs:
             torch_cuda_arch_list: "9.0+PTX"
             dockerfile: "Dockerfile-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "130"
-            cuda_version: 13.0.0
-            cudnn_version: ""
-            python_version: "3.12"
-            pytorch: 2.9.1
-            torch_cuda_arch_list: "9.0+PTX"
-            dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64,linux/arm64"
 #          - cuda: "128"
 #            cuda_version: 12.8.1
 #            cudnn_version: ""
@@ -157,14 +141,6 @@ jobs:
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "129"
-            cuda_version: 12.9.1
-            cudnn_version: ""
-            python_version: "3.12"
-            pytorch: 2.9.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "130"
             cuda_version: 13.0.0
             cudnn_version: ""
@@ -173,14 +149,6 @@ jobs:
             torch_cuda_arch_list: "9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "130"
-            cuda_version: 13.0.0
-            cudnn_version: ""
-            python_version: "3.12"
-            pytorch: 2.9.1
-            torch_cuda_arch_list: "9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/main.yml

@@ -34,12 +34,6 @@ jobs:
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
             is_latest: true
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
-            pytorch: 2.9.1
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 130
             cuda_version: 13.0.0
             python_version: "3.11"
@@ -112,12 +106,6 @@ jobs:
             axolotl_extras:
             is_latest: true
             platforms: "linux/amd64,linux/arm64"
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
-            pytorch: 2.9.1
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 130
             cuda_version: 13.0.0
             python_version: "3.11"

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

@@ -35,19 +35,14 @@ jobs:
             pytorch: 2.8.0
             axolotl_extras: fbgemm-gpu
             num_gpus: 2
+            nightly_build: "true"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.1
-            axolotl_extras: "fbgemm-gpu"
+            axolotl_extras: fbgemm-gpu
             num_gpus: 2
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
-            pytorch: 2.9.1
-            axolotl_extras: "fbgemm-gpu"
-            num_gpus: 2
-            dockerfile: "Dockerfile-uv.jinja"
+            nightly_build: "true"
           - cuda: 130
             cuda_version: 13.0.0
             python_version: "3.11"
@@ -55,6 +50,7 @@ jobs:
             axolotl_extras:
 #            axolotl_extras: fbgemm-gpu
             num_gpus: 2
+            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:
@@ -76,8 +72,8 @@ jobs:
           echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+          echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
           echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
-          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
           modal run -m cicd.multigpu

.github/workflows/pypi.yml

@@ -40,7 +40,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip3 install wheel packaging==26.0
+          pip3 install wheel packaging==23.2
           pip3 install --no-build-isolation -e .
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 

.github/workflows/tests-nightly.yml

@@ -48,7 +48,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==26.0 setuptools==75.8.0 wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
 
       - name: Install PyTorch
         run: |

.github/workflows/tests.yml

@@ -54,13 +54,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.11", "3.12"]
+        python_version: ["3.11"]
         pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
-        exclude:
-          - python_version: "3.12"
-            pytorch_version: "2.8.0"
-          - python_version: "3.12"
-            pytorch_version: "2.9.0"
     timeout-minutes: 20
 
     steps:
@@ -87,7 +82,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==26.0 setuptools==75.8.0 wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
 
       - name: Install PyTorch
         run: |
@@ -115,10 +110,10 @@ jobs:
 
       - name: Pre-Download dataset fixture
         run: |
-          hf download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
+          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
       - name: Run tests
         run: |
@@ -132,7 +127,7 @@ jobs:
           pytest -v --durations=10 tests/cli/ --cov=axolotl --cov-append --cov-report=xml
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v5
@@ -149,13 +144,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.11", "3.12"]
+        python_version: ["3.11"]
         pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
-        exclude:
-          - python_version: "3.12"
-            pytorch_version: "2.8.0"
-          - python_version: "3.12"
-            pytorch_version: "2.9.0"
     timeout-minutes: 20
 
     steps:
@@ -182,7 +172,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==26.0 setuptools==75.8.0 setuptools_scm build wheel psutil
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 setuptools_scm build wheel psutil
 
       - name: Install PyTorch
         run: |
@@ -210,7 +200,7 @@ jobs:
           axolotl --help
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
       - name: Run tests
         run: |
@@ -219,10 +209,10 @@ jobs:
           pytest -v --durations=10 tests/cli/
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
   gate-skip-e2e:
-    needs: [pre-commit]
+    needs: [pre-commit, pytest, pytest-sdist]
     runs-on: ubuntu-latest
     outputs:
       skip: ${{ steps.compute.outputs.skip }}
@@ -258,16 +248,16 @@ jobs:
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
-    needs: [pre-commit, pytest]
+    needs: [pre-commit, pytest, pytest-sdist, gate-skip-e2e]
 
     strategy:
       fail-fast: false
       matrix:
         include:
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
-            pytorch: 2.9.1
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.8.0
             num_gpus: 1
             axolotl_extras:
             dockerfile: "Dockerfile-uv.jinja"
@@ -369,9 +359,9 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:

.runpod/src/config/config.yaml

@@ -224,6 +224,9 @@
 # eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 # eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
 
+# # Save model as safetensors (require safetensors package)
+# save_safetensors:
+
 # # Whether to mask out or include the human's prompt from the training labels
 # train_on_inputs: false
 # # Group similarly sized data to minimize padding.
@@ -509,6 +512,7 @@ profiler_steps: ${PROFILER_STEPS}
 loss_watchdog_threshold: ${LOSS_WATCHDOG_THRESHOLD}
 loss_watchdog_patience: ${LOSS_WATCHDOG_PATIENCE}
 
+save_safetensors: ${SAVE_SAFETENSORS}
 train_on_inputs: ${TRAIN_ON_INPUTS}
 group_by_length: ${GROUP_BY_LENGTH}
 gradient_checkpointing: ${GRADIENT_CHECKPOINTING}

README.md

@@ -88,7 +88,7 @@ Features:
 #### Using pip
 
 ```bash
-pip3 install -U packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 
 # Download example axolotl configs, deepspeed configs

VERSION

@@ -1 +1 @@
-0.15.0.dev0
+0.14.0.dev0

cicd/Dockerfile-uv.jinja

@@ -31,7 +31,7 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
-RUN uv pip install packaging==26.0 setuptools==75.8.0
+RUN uv pip install packaging==23.2 setuptools==75.8.0
 RUN uv pip install torchvision
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         uv pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \

cicd/Dockerfile.jinja

@@ -32,7 +32,7 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
-RUN pip install packaging==26.0 setuptools==75.8.0 psutil
+RUN pip install packaging==23.2 setuptools==75.8.0 psutil
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \

cicd/multigpu.py

@@ -17,8 +17,7 @@ template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
 template_env = jinja2.Environment(
     loader=template_loader, autoescape=select_autoescape()
 )
-dockerfile = os.environ.get("E2E_DOCKERFILE", "Dockerfile.jinja")
-df_template = template_env.get_template(dockerfile)
+df_template = template_env.get_template("Dockerfile.jinja")
 
 df_args = {
     "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
@@ -28,11 +27,8 @@ df_args = {
     "CUDA": os.environ.get("CUDA", "126"),
     "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
     "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
-    "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
     "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
     "HF_HOME": "/workspace/data/huggingface-cache/hub",
-    "PYTHONUNBUFFERED": os.environ.get("PYTHONUNBUFFERED", "1"),
-    "DEEPSPEED_LOG_LEVEL": os.environ.get("DEEPSPEED_LOG_LEVEL", "WARNING"),
 }
 
 dockerfile_contents = df_template.render(**df_args)

cicd/multigpu.sh

@@ -2,7 +2,7 @@
 set -e
 
 # Only run two tests at a time to avoid OOM on GPU (with coverage collection)
-pytest -v --durations=10 -n2 --maxfail=3 \
+pytest -v --durations=10 -n2 --maxfail=4 \
   --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/ \
   --ignore=/workspace/axolotl/tests/e2e/multigpu/patched/ \
   /workspace/axolotl/tests/e2e/multigpu/ \

docker/Dockerfile-base

@@ -43,7 +43,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 
-RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==26.0 setuptools==75.8.0 wheel psutil && \
+RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel psutil && \
     python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} torchvision --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
     python3 -m pip cache purge
 

docker/Dockerfile-base-nightly

@@ -30,7 +30,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 
-RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==26.0 setuptools==75.8.0 wheel && \
+RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
     python3 -m pip install --no-cache-dir -U torch --extra-index-url https://download.pytorch.org/whl/nightly/cu$CUDA && \
     python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
     python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" && \

docs/amd_hpc.qmd

@@ -86,7 +86,7 @@ export HF_DATASETS_OFFLINE=1
 Download a base model using the Hugging Face CLI:
 
 ```bash
-hf download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
+huggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
 ```
 
 ### 10. Create Axolotl Configuration

docs/installation.qmd

@@ -165,7 +165,7 @@ We recommend using WSL2 (Windows Subsystem for Linux) or Docker.
    ```
 4. (Optional) Login to Hugging Face:
    ```{.bash}
-   hf auth login
+   huggingface-cli login
    ```
 
 ## Troubleshooting {#sec-troubleshooting}

docs/rlhf.qmd

@@ -17,7 +17,6 @@ feedback. Various methods include, but not limited to:
 - [Kahneman-Tversky Optimization (KTO)](#kto)
 - [Odds Ratio Preference Optimization (ORPO)](#orpo)
 - [Group Relative Policy Optimization (GRPO)](#grpo)
-- [Group Reward-Decoupled Policy Optimization (GDPO)](#gdpo)
 
 
 ## RLHF using Axolotl
@@ -721,102 +720,6 @@ trl:
 
 For more information, see [GRPO docs](https://huggingface.co/docs/trl/v0.17.0/en/grpo_trainer#loss-types).
 
-### GDPO
-
-GDPO (Group Reward-Decoupled Policy Optimization) extends GRPO for multi-reward training. It addresses the **reward advantage collapse** problem by normalizing each reward function independently before combining them.
-
-::: {.callout-tip}
-Use GDPO when training with multiple reward functions. For single reward, GRPO and GDPO produce equivalent results.
-:::
-
-Paper: [https://arxiv.org/pdf/2501.05242](https://arxiv.org/pdf/2501.05242)
-
-GDPO uses TRL's native `multi_objective_aggregation` parameter under the hood. When you set `rl: gdpo`, axolotl automatically configures TRL to use `normalize_then_sum` aggregation.
-
-```yaml
-base_model: Qwen/Qwen2.5-1.5B-Instruct
-
-vllm:
-    host: 0.0.0.0
-    port: 8000
-    tensor_parallel_size: 2
-    gpu_memory_utilization: 0.85
-
-rl: gdpo
-
-trl:
-    beta: 0.001
-    max_completion_length: 256
-    use_vllm: true
-    num_generations: 4
-    reward_funcs:
-        - rewards.format_reward
-        - rewards.correctness_reward
-    reward_weights: [1.0, 2.0]
-
-datasets:
-    - path: openai/gsm8k
-      name: main
-      type: rewards.oai_gsm8k_transform
-```
-
-You can also use GRPO with explicit aggregation control:
-
-```yaml
-rl: grpo
-trl:
-    multi_objective_aggregation: normalize_then_sum  # GDPO behavior
-    # or: sum_then_normalize  # Default GRPO behavior
-```
-
-#### GDPO vs GRPO
-
-| Aspect | GRPO | GDPO |
-|--------|------|------|
-| **Aggregation** | `sum_then_normalize` | `normalize_then_sum` |
-| **Multi-reward** | May collapse advantages | Preserves reward signals |
-| **Single reward** | Standard behavior | Equivalent to GRPO |
-
-#### Why GDPO?
-
-When using multiple rewards with GRPO, different reward combinations can produce identical advantages:
-
-```
-# Example: format + correctness rewards
-[format=0, correct=3] → sum=3
-[format=1, correct=2] → sum=3  ← GRPO sees these as equal!
-[format=2, correct=1] → sum=3
-[format=3, correct=0] → sum=3
-```
-
-GDPO normalizes each reward independently, preserving their relative differences.
-
-#### Reward Functions
-
-GDPO uses the same reward function format as GRPO:
-
-```python
-# rewards.py
-def format_reward(completions, **kwargs) -> list[float]:
-    return [1.0 if len(c) > 10 else 0.0 for c in completions]
-
-def correctness_reward(completions, answers, **kwargs) -> list[float]:
-    rewards = []
-    for completion, answer in zip(completions, answers):
-        # Your scoring logic here
-        rewards.append(score)
-    return rewards
-```
-
-#### Sequence Parallelism
-
-GDPO supports sequence parallelism for long-context training:
-
-```yaml
-rl: gdpo
-context_parallel_size: 2
-```
-
 ### SimPO
 
 SimPO uses [CPOTrainer](https://huggingface.co/docs/trl/main/en/cpo_trainer) but with alternative loss function.

examples/apertus/README.md

@@ -15,7 +15,7 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy

examples/arcee/README.md

@@ -17,7 +17,7 @@ Thanks to the team at Arcee.ai for using Axolotl in supervised fine-tuning the A
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy

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@e39ca1d\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2\""
    ]
   },
   {

examples/devstral/README.md

@@ -16,7 +16,7 @@ Thanks to the team at MistralAI for giving us early access to prepare for this r
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 

examples/eaft/eaft-example.yml

@@ -1,77 +0,0 @@
-base_model: google/gemma-3-1b-it
-
-model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
-
-# gemma3 doesn't seem to play nice with ddp
-ddp_find_unused_parameters: true
-
-chat_template: gemma3
-eot_tokens:
-  - <end_of_turn>
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-dataset_prepared_path:
-val_set_size: 0
-output_dir: ./outputs/eaft-gemma-3-1b
-
-use_eaft: true
-eaft_alpha: 1.0
-eaft_k: 20
-
-sequence_len: 1024
-sample_packing: false
-
-adapter:
-lora_model_dir:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-eval_batch_size: 1
-max_steps: 1000
-evaluation_strategy: "no"
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-5
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-debug:
-deepspeed:
-fsdp:
-fsdp_config:
-special_tokens:

examples/gemma3n/README.md

@@ -10,7 +10,7 @@ Gemma-3n is a family of multimodal models from Google found on [HuggingFace](htt
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 

examples/glm4.7-flash/README.md

@@ -1,40 +0,0 @@
-# Finetune Z.ai's GLM-4.7-Flash with Axolotl
-
-[GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) is a 30B-A3B MoE model.
-
-This guide shows how to fine-tune it with Axolotl.
-
-## 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/glm4.7-flash/glm4.7-flash-qlora.yaml
-```
-
-This config uses about X GiB VRAM.
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### TIPS
-
-- For inference, the official Z.ai team recommends `top_p: 0.95`, `temperature: 1.0`, and `max_new_tokens: 131072`.
-- 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).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [GLM-4.7-Flash on HuggingFace](https://huggingface.co/zai-org/GLM-4.7-Flash)
-- [GLM-4.7 Blog](https://z.ai/blog/glm-4.7)
-- [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/glm4.7-flash/glm4.7-flash-qlora.yaml

@@ -1,63 +0,0 @@
-base_model: zai-org/GLM-4.7-Flash
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-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: glm-4.7-flash
-wandb_entity:
-wandb_watch:
-wandb_name: qlora
-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/gpt-oss/README.md

@@ -14,7 +14,7 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 

examples/granite4/README.md

@@ -15,7 +15,7 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy

examples/hunyuan/README.md

@@ -13,7 +13,7 @@ Tencent released a family of opensource models called HunYuan with varying param
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy

examples/jamba/qlora_fsdp_large.yaml

@@ -19,6 +19,7 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: jamba-large-fsdp-qlora-ft
+save_safetensors: true
 adapter: qlora
 sequence_len: 2048
 sample_packing: true

examples/llama-3/qlora-1b-gdpo.yaml

@@ -1,68 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B-Instruct
-
-chat_template: llama3
-
-rl: gdpo
-
-trl:
-  beta: 0.001
-  max_completion_length: 128
-  num_generations: 2
-  temperature: 0.7
-  top_p: 0.95
-
-  use_vllm: false
-
-
-  multi_objective_aggregation: normalize_then_sum
-
-  reward_funcs:
-    - rwd.format_reward
-    - rwd.correctness_reward
-  reward_weights: [1.0, 2.0]
-
-  log_completions: true
-  num_completions_to_print: 3
-  scale_rewards: true
-
-datasets:
-  - path: openai/gsm8k
-    name: main
-    split: train[:1000]
-    type: rwd.gsm8k_transform
-
-val_set_size: 0.0
-output_dir: ./outputs/llama3-gdpo-out
-
-sequence_len: 512
-sample_packing: false
-pad_to_sequence_len: false
-
-gradient_accumulation_steps: 8
-micro_batch_size: 1
-num_epochs: 1
-max_steps: 100
-
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-5
-weight_decay: 0.01
-warmup_steps: 10
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-
-flash_attention: true
-logging_steps: 1
-save_steps: 50
-save_safetensors: true
-
-special_tokens:
-  pad_token: "<|end_of_text|>"
-
-
-seed: 42

examples/llama-3/qlora-fsdp-405b.yaml

@@ -12,6 +12,7 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: ./outputs/out/qlora-llama3_1-405b
+save_safetensors: true
 
 adapter: qlora
 

examples/magistral/README.md

@@ -14,7 +14,7 @@ Thanks to the team at MistralAI for giving us early access to prepare for these
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.7.0 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 

examples/mamba/config.yml

@@ -47,5 +47,6 @@ saves_per_epoch: 1
 weight_decay: 0.0
 special_tokens:
 tokens:
+save_safetensors: False
 
 # save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qwen3-next/README.md

@@ -15,7 +15,7 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy

examples/trinity/README.md

@@ -8,15 +8,13 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 1. Install Axolotl following the main from 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. Run the finetuning example:
+2. Run the finetuning example:
 
     ```bash
     axolotl train examples/trinity/trinity-nano-preview-qlora.yaml
     ```
 
-This config uses about 24.9 GiB VRAM (w/o CCE).
+This config uses about 24.9 GiB VRAM.
 
 Let us know how it goes. Happy finetuning! 🚀
 
@@ -31,6 +29,10 @@ Let us know how it goes. Happy finetuning! 🚀
 
 Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
 
+## Limitations
+
+**Cut Cross Entropy (CCE)**: Currently not supported. We plan to include CCE support for Trinity in the near future.
+
 ## Related Resources
 
 - [Trinity Blog](https://www.arcee.ai/blog/the-trinity-manifesto)

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

@@ -1,11 +1,13 @@
 base_model: arcee-ai/Trinity-Nano-Preview
+trust_remote_code: true
 revision_of_model: 2ee94b0
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+# CCE - N/A as of now
+# plugins:
+#   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
 
 load_in_8bit: false
 load_in_4bit: true

examples/voxtral/README.md

@@ -12,7 +12,7 @@ Thanks to the team at MistralAI for giving us early access to prepare for this r
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools>=64", "wheel", "setuptools_scm>=8", "packaging==26.0"]
+requires = ["setuptools>=64", "wheel", "setuptools_scm>=8", "packaging==23.2"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -60,6 +60,3 @@ indent-style = "space"
 skip-magic-trailing-comma = false
 line-ending = "auto"
 docstring-code-format = false
-
-[tool.uv.extra-build-dependencies]
-axolotl = ["huggingface_hub"]

requirements.txt

@@ -8,18 +8,18 @@ xformers>=0.0.23.post1
 liger-kernel==0.6.4
 # END section
 
-packaging==26.0
-huggingface_hub>=1.1.7
+packaging==23.2
+
+huggingface_hub>=0.36.0
 peft>=0.18.1
 tokenizers>=0.22.1
-transformers==5.0.0
+transformers==4.57.6
 accelerate==1.12.0
 datasets==4.5.0
 deepspeed>=0.18.3
-trl==0.27.1
+trl==0.25.1
 hf_xet==1.2.0
 kernels==0.11.5
-
 trackio>=0.13.0
 typing-extensions>=4.15.0
 
@@ -72,4 +72,4 @@ axolotl-contribs-mit==0.0.6
 # telemetry
 posthog==6.7.11
 
-mistral-common==1.8.8
+mistral-common==1.8.6

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@e39ca1d"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"'
 )

setup.py

@@ -78,11 +78,6 @@ def parse_requirements(extras_require_map):
                 extras_require_map["vllm"] = ["vllm==0.11.1"]
                 if not install_xformers:
                     _install_requires.pop(_install_requires.index(xformers_version))
-                extras_require_map["vllm"] = ["vllm==0.13.0"]
-                if patch == 0:
-                    extras_require_map["vllm"] = ["vllm==0.13.0"]
-                else:
-                    extras_require_map["vllm"] = ["vllm==0.14.0"]
             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/__init__.py

@@ -5,6 +5,6 @@ import os
 from axolotl.logging_config import configure_logging
 
 os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
-os.environ.setdefault("HF_XET_HIGH_PERFORMANCE", "1")
+os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1")
 
 configure_logging()

src/axolotl/cli/checks.py

@@ -44,7 +44,7 @@ def check_user_token() -> bool:
         return bool(user_info)
     except LocalTokenNotFoundError:
         LOG.warning(
-            "Error verifying HuggingFace token. Remember to log in using `hf auth login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
+            "Error verifying HuggingFace token. Remember to log in using `huggingface-cli login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
         )
         return False
     except HTTPError:

src/axolotl/cli/merge_lora.py

@@ -24,6 +24,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         cfg: Dictionary mapping `axolotl` config keys to values.
     """
     model, tokenizer, processor = load_model_and_tokenizer(cfg=cfg)
+    safe_serialization = cfg.save_safetensors is True
 
     LOG.info("Running merge of LoRA with base model...")
     model = model.merge_and_unload(progressbar=True)
@@ -41,6 +42,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         LOG.info(f"Saving merged model to: {str(Path(cfg.output_dir) / 'merged')}...")
         model.save_pretrained(
             str(Path(cfg.output_dir) / "merged"),
+            safe_serialization=safe_serialization,
             progressbar=True,
         )
         tokenizer.save_pretrained(

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -14,6 +14,8 @@ from accelerate import PartialState
 from accelerate.utils import (
     SAFE_WEIGHTS_INDEX_NAME,
     SAFE_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
     is_torch_version,
 )
 from huggingface_hub import split_torch_state_dict_into_shards
@@ -38,15 +40,17 @@ class BFloat16CastPlanner(_EmptyStateDictLoadPlanner):
 def _distributed_checkpoint_to_merged_weights(
     checkpoint_dir: Union[str, Path],
     save_path: str,
+    safe_serialization: bool = False,
     max_shard_size: str = "5GB",
 ) -> Path:
     """
     Passthrough to `torch.distributed.checkpoint.format_utils.dcp_to_torch_save`. Will
-    save under `save_path` as `model.safetensors`.
+    save under `save_path` as either `model.safetensors` or `pytorch_model.bin`.
 
     Args:
         checkpoint_dir: Directory where distributed checkpoint is saved.
         save_path: Path to save model to.
+        safe_serialization: Whether to save in safetensors format.
         max_shard_size: Max size of model shards to save.
 
     Returns:
@@ -72,7 +76,11 @@ def _distributed_checkpoint_to_merged_weights(
         if isinstance(value, torch.Tensor) and value.dtype != torch.bfloat16:
             state_dict[key] = value.to(torch.bfloat16)
 
-    filename_pattern = SAFE_WEIGHTS_NAME.replace(".safetensors", "{suffix}.safetensors")
+    weights_name = SAFE_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
+
+    filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
+        ".safetensors", "{suffix}.safetensors"
+    )
     state_dict_split = split_torch_state_dict_into_shards(
         state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
     )
@@ -90,12 +98,19 @@ def _distributed_checkpoint_to_merged_weights(
 
     for shard_file, tensors in filename_to_tensors:
         shard = {tensor: state_dict[tensor] for tensor in tensors}
-        safe_save_file(
-            shard, os.path.join(save_path_, shard_file), metadata={"format": "pt"}
-        )
+
+        if safe_serialization:
+            safe_save_file(
+                shard, os.path.join(save_path_, shard_file), metadata={"format": "pt"}
+            )
+        else:
+            torch.save(shard, os.path.join(save_path_, shard_file))
 
     if index is not None:
-        save_index_file = os.path.join(save_path_, SAFE_WEIGHTS_INDEX_NAME)
+        save_index_file = (
+            SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
+        )
+        save_index_file = os.path.join(save_path_, save_index_file)
         # Save the index as well
         with open(save_index_file, "w", encoding="utf-8") as fout:
             content = json.dumps(index, indent=2, sort_keys=True) + "\n"
@@ -108,11 +123,13 @@ def _distributed_checkpoint_to_merged_weights(
 def merge_fsdp_weights(
     checkpoint_dir: str,
     output_path: str,
+    safe_serialization: bool = False,
     remove_checkpoint_dir: bool = False,
 ):
     """
     Merge the weights from sharded FSDP model checkpoints into a single combined checkpoint. Should be used if
-    `SHARDED_STATE_DICT` was used for the model. Weights will be saved to `{output_path}/model.safetensors`.
+    `SHARDED_STATE_DICT` was used for the model. Weights will be saved to `{output_path}/model.safetensors` if
+    `safe_serialization` else `pytorch_model.bin`.
 
     Note: this is a CPU-bound process.
 
@@ -121,6 +138,8 @@ def merge_fsdp_weights(
             The directory containing the FSDP checkpoints (can be either the model or optimizer).
         output_path (`str`):
             The path to save the merged checkpoint.
+        safe_serialization (`bool`, *optional*, defaults to `True`):
+            Whether to save the merged weights with safetensors (recommended).
         remove_checkpoint_dir (`bool`, *optional*, defaults to `False`):
             Whether to remove the checkpoint directory after merging.
 
@@ -158,7 +177,7 @@ def merge_fsdp_weights(
     if state.is_main_process:
         LOG.info(f"Merging FSDP weights from {checkpoint_dir_}")
         save_path = _distributed_checkpoint_to_merged_weights(
-            checkpoint_dir_, output_path
+            checkpoint_dir_, output_path, safe_serialization
         )
         LOG.info(f"Successfully merged FSDP weights and saved to {save_path}")
         if remove_checkpoint_dir:
@@ -191,6 +210,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     merge_fsdp_weights(
         checkpoint_dir=str(fsdp_dir),
         output_path=output_path,
+        safe_serialization=True,
     )
     state = PartialState()
     state.wait_for_everyone()

src/axolotl/cli/quantize.py

@@ -102,10 +102,12 @@ def do_quantize(
     LOG.info(f"Saving quantized model to: {str(Path(output_dir) / 'quantized')}.")
     model.save_pretrained(
         str(Path(output_dir) / "quantized"),
+        safe_serialization=False,
         progressbar=True,
     )
     tokenizer.save_pretrained(
         str(Path(output_dir) / "quantized"),
+        safe_serialization=False,
         progressbar=True,
         save_jinja_files=cfg.tokenizer_save_jinja_files,
     )
@@ -119,7 +121,7 @@ def do_quantize(
             hub_model_id.rstrip("-")
             + f"-{quantization_config_to_str[type(quantization_config)]}"
         )
-        model.push_to_hub(hub_model_id)
+        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)

src/axolotl/core/builders/base.py

@@ -216,7 +216,7 @@ class TrainerBuilderBase(abc.ABC):
     def _configure_warmup_and_logging(
         self, total_num_steps: int, training_args_kwargs: dict
     ):
-        warmup_steps: int | float = 0
+        warmup_steps = 0
         warmup_ratio = 0.0
         if self.cfg.warmup_steps is not None:
             warmup_steps = self.cfg.warmup_steps
@@ -230,10 +230,6 @@ class TrainerBuilderBase(abc.ABC):
         else:
             warmup_ratio = 0.03
 
-        # transformers v5
-        if warmup_ratio > 0.0 and warmup_steps == 0:
-            warmup_steps = warmup_ratio
-
         if warmup_steps == 1:
             warmup_steps = 2
 
@@ -246,6 +242,7 @@ class TrainerBuilderBase(abc.ABC):
                 else max(min(int(0.005 * total_num_steps), 10), 1)
             )
 
+        training_args_kwargs["warmup_ratio"] = warmup_ratio
         training_args_kwargs["warmup_steps"] = warmup_steps
 
     def _configure_precision_settings(self, training_args_kwargs: dict):
@@ -533,7 +530,9 @@ class TrainerBuilderBase(abc.ABC):
             "loraplus_lr_ratio",
             "loraplus_lr_embedding",
             "output_dir",
+            "save_safetensors",
             "save_only_model",
+            "include_tokens_per_second",
             "weight_decay",
             "seed",
             "dion_momentum",
@@ -546,7 +545,6 @@ class TrainerBuilderBase(abc.ABC):
 
         arg_map = {
             "dion_learning_rate": "dion_lr",
-            "include_num_input_tokens_seen": "include_tokens_per_second",
         }
         for kwarg, cfg_arg in arg_map.items():
             if hasattr(self.cfg, cfg_arg) and getattr(self.cfg, cfg_arg) is not None:

src/axolotl/core/builders/causal.py

@@ -373,18 +373,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = multiple
 
-        if self.cfg.use_eaft:
-            from functools import partial
-
-            from axolotl.monkeypatch.loss.eaft import eaft_loss
-
-            configured_eaft_loss = partial(
-                eaft_loss,
-                alpha=self.cfg.eaft_alpha if self.cfg.eaft_alpha is not None else 1.0,
-                k=self.cfg.eaft_k if self.cfg.eaft_k is not None else 20,
-            )
-            trainer_kwargs["compute_loss_func"] = configured_eaft_loss
-
         trainer_cls = self._get_trainer_cls()
 
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
@@ -449,9 +437,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 or self.cfg.micro_batch_size > 1
             ):
                 return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
-            if not (self.cfg.sample_packing and self.cfg.pretrain_multipack_attn) or (
-                self.cfg.micro_batch_size == 1 and is_eval is False
-            ):
+            if not (self.cfg.sample_packing and self.cfg.pretrain_multipack_attn):
                 return None
 
         if self.cfg.model_config_type == "mamba":

src/axolotl/core/builders/rl.py

@@ -52,11 +52,12 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         trainer_cls = None
         trainer_cls_args = [self.model]
 
-        if self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
+        if self.cfg.rl is RLType.GRPO:
             trainer_cls = GRPOStrategy.get_trainer_class(
                 sequence_parallel=self.cfg.context_parallel_size > 1
             )
             trainer_cls_args.extend(GRPOStrategy.set_trainer_args(self.cfg))
+
             trainer_kwargs.update(GRPOStrategy.set_trainer_kwargs(self.cfg))
 
         elif self.cfg.rl in [RLType.DPO, RLType.IPO]:
@@ -146,8 +147,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
         elif self.cfg.rl is RLType.KTO:
             training_args_cls = AxolotlKTOConfig
-            # KTOConfig in TRL >= 0.27.0 no longer accepts max_prompt_length
-            blocklist_args_kwargs = ["max_prompt_length"]
 
             training_args_kwargs["desirable_weight"] = (
                 self.cfg.kto_desirable_weight or 1.0
@@ -156,14 +155,10 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
                 self.cfg.kto_undesirable_weight or 1.0
             )
 
-        elif self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
+        elif self.cfg.rl is RLType.GRPO:
             training_args_cls = GRPOStrategy.get_training_args_class()
             training_args_kwargs.update(GRPOStrategy.set_training_args_kwargs(self.cfg))
             blocklist_args_kwargs = GRPOStrategy.get_blocklist_args_kwargs()
-            if self.cfg.rl is RLType.GDPO:
-                training_args_kwargs.setdefault(
-                    "multi_objective_aggregation", "normalize_then_sum"
-                )
 
         elif self.cfg.rl in [RLType.DPO, RLType.IPO]:
             training_args_cls = AxolotlDPOConfig

src/axolotl/core/trainers/base.py

@@ -25,7 +25,7 @@ from torch.utils.data import (
 from transformers import PreTrainedModel, Trainer
 from transformers.trainer import TRAINING_ARGS_NAME
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, has_length, seed_worker
-from transformers.utils import SAFE_WEIGHTS_NAME, is_peft_available
+from transformers.utils import SAFE_WEIGHTS_NAME, WEIGHTS_NAME, is_peft_available
 from trl.trainer.utils import pad_to_length
 from typing_extensions import override
 
@@ -738,38 +738,43 @@ class AxolotlTrainer(
                 ).save_pretrained(
                     output_dir,
                     state_dict=state_dict,
+                    safe_serialization=self.args.save_safetensors,
                 )
             else:
                 LOG.info(
                     "Trainer.model is not a `PreTrainedModel`, only saving its state dict."
                 )
-                safetensors.torch.save_file(
-                    state_dict,
-                    os.path.join(output_dir, SAFE_WEIGHTS_NAME),
-                    metadata={"format": "pt"},
-                )
+                if self.args.save_safetensors:
+                    safetensors.torch.save_file(
+                        state_dict,
+                        os.path.join(output_dir, SAFE_WEIGHTS_NAME),
+                        metadata={"format": "pt"},
+                    )
+                else:
+                    torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
         else:
             self.model.save_pretrained(
                 output_dir,
                 state_dict=state_dict,
+                safe_serialization=self.args.save_safetensors,
                 is_main_process=self.accelerator.is_main_process,
             )
 
-        if self.processing_class is not None:
-            self.processing_class.save_pretrained(output_dir)
-        elif (
-            self.data_collator is not None
-            and hasattr(self.data_collator, "tokenizer")
-            and self.data_collator.tokenizer is not None
-        ):
-            LOG.info(
-                "Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`"
-            )
-            save_jinja_files = True
-            if self.axolotl_cfg:
-                save_jinja_files = self.axolotl_cfg.tokenizer_save_jinja_files
-            self.data_collator.tokenizer.save_pretrained(
-                output_dir, save_jinja_files=save_jinja_files
-            )
-        # Good practice: save your training arguments together with the trained model
-        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
+            if self.processing_class is not None:
+                self.processing_class.save_pretrained(output_dir)
+            elif (
+                self.data_collator is not None
+                and hasattr(self.data_collator, "tokenizer")
+                and self.data_collator.tokenizer is not None
+            ):
+                LOG.info(
+                    "Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`"
+                )
+                save_jinja_files = True
+                if self.axolotl_cfg:
+                    save_jinja_files = self.axolotl_cfg.tokenizer_save_jinja_files
+                self.data_collator.tokenizer.save_pretrained(
+                    output_dir, save_jinja_files=save_jinja_files
+                )
+            # Good practice: save your training arguments together with the trained model
+            torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

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

@@ -129,11 +129,6 @@ class GRPOStrategy:
         if trl.rollout_func:
             grpo_args_kwargs["rollout_func"] = cls.get_rollout_func(trl.rollout_func)
 
-        if trl.multi_objective_aggregation is not None:
-            grpo_args_kwargs["multi_objective_aggregation"] = (
-                trl.multi_objective_aggregation
-            )
-
         return grpo_args_kwargs
 
     @classmethod

src/axolotl/core/trainers/trl.py

@@ -1,10 +1,12 @@
 """Module for TRL RL trainers"""
 
-from trl import RewardTrainer
-from trl.experimental.cpo import CPOTrainer
-from trl.experimental.kto import KTOTrainer
-from trl.experimental.orpo import ORPOTrainer
-from trl.experimental.prm import PRMTrainer
+from trl import (
+    CPOTrainer,
+    KTOTrainer,
+    ORPOTrainer,
+    PRMTrainer,
+    RewardTrainer,
+)
 
 from axolotl.core.trainers.mixins import DistributedParallelMixin, RngLoaderMixin
 from axolotl.core.trainers.mixins.optimizer import OptimizerInitMixin, OptimizerMixin

src/axolotl/core/training_args.py

@@ -8,11 +8,7 @@ from dataclasses import dataclass, field
 from typing import Optional, Type
 
 from transformers import TrainingArguments
-from trl import RewardConfig
-from trl.experimental.cpo import CPOConfig
-from trl.experimental.kto import KTOConfig
-from trl.experimental.orpo import ORPOConfig
-from trl.experimental.prm import PRMConfig
+from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
 from axolotl.integrations.config import merge_training_args
 

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@e39ca1d"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"
 ```
 
 ## Usage
@@ -31,13 +31,11 @@ plugins:
 
 ## Supported Models
 
-- afmoe
 - apertus
 - arcee
 - cohere
 - cohere2
 - deepseek_v3
-- exaone4
 - gemma
 - gemma2
 - gemma3
@@ -47,11 +45,8 @@ plugins:
 - glm
 - glm4
 - glm4_moe
-- glm4_moe_lite
-- glm46v
 - glm4v
 - glm4v_moe
-- glm_image
 - gpt_oss
 - granite
 - granitemoe

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@e39ca1d"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"`'
 )
 
 

src/axolotl/integrations/kernels/__init__.py

@@ -1,7 +0,0 @@
-from .args import KernelsArgs
-from .plugin import KernelsPlugin
-
-__all__ = [
-    "KernelsArgs",
-    "KernelsPlugin",
-]

src/axolotl/integrations/kernels/args.py

@@ -1,35 +0,0 @@
-from pydantic import BaseModel, model_validator
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-class KernelsArgs(BaseModel):
-    use_scattermoe: bool | None = True
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_use_kernels(cls, data):
-        if data.get("use_kernels") is not True:
-            LOG.warning(
-                "`use_kernels` must be set to True to use this. Automatically setting it to True."
-            )
-            data["use_kernels"] = True
-
-        return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_experts_implementation(cls, data):
-        experts_implementation = data.get("experts_implementation")
-        if experts_implementation is None:
-            # transformers may default to batched_mm when unset
-            data["experts_implementation"] = "eager"
-        elif experts_implementation != "eager":
-            LOG.warning(
-                "`experts_implementation` must be set to 'eager' to use this. Automatically setting it to 'eager'."
-            )
-            data["experts_implementation"] = "eager"
-
-        return data

src/axolotl/integrations/kernels/plugin.py

@@ -1,61 +0,0 @@
-from kernels import (
-    LayerRepository,
-    Mode,
-    register_kernel_mapping,
-    replace_kernel_forward_from_hub,
-)
-
-from axolotl.integrations.base import BasePlugin
-from axolotl.utils.callbacks.models import get_causal_lm_model_cls_prefix
-
-
-class KernelsPlugin(BasePlugin):
-    def get_input_args(self):
-        return "axolotl.integrations.kernels.KernelsArgs"
-
-    def pre_model_load(self, cfg):
-        if cfg.use_scattermoe:
-            self._register_kernels()
-            self._kernelize_model(cfg.model_config_type)
-
-    def _register_kernels(self):
-        register_kernel_mapping(
-            {
-                "HFScatterMoEParallelExperts": {
-                    "cuda": {
-                        Mode.TRAINING: LayerRepository(
-                            repo_id="axolotl-ai-co/scattermoe",
-                            layer_name="HFScatterMoEGatedMLP",
-                        ),
-                        Mode.INFERENCE: LayerRepository(
-                            repo_id="axolotl-ai-co/scattermoe",
-                            layer_name="HFScatterMoEGatedMLP",
-                        ),
-                    },
-                }
-            }
-        )
-
-    def _kernelize_model(self, model_type: str):
-        if model_type == "olmoe":
-            from transformers.models.olmoe.modeling_olmoe import OlmoeSparseMoeBlock
-
-            replace_kernel_forward_from_hub(
-                OlmoeSparseMoeBlock, "HFScatterMoEParallelExperts"
-            )
-        else:
-            try:
-                model_moe_cls = get_model_moe_block(model_type)
-                replace_kernel_forward_from_hub(
-                    model_moe_cls, "HFScatterMoEParallelExperts"
-                )
-            except Exception as err:
-                raise ValueError(f"Unsupported model type: {model_type}") from err
-
-
-def get_model_moe_block(model_type: str):
-    module_path = f"transformers.models.{model_type}.modeling_{model_type}"
-    model_cls_prefix, _ = get_causal_lm_model_cls_prefix(model_type)
-    module = __import__(module_path, fromlist=[f"{model_cls_prefix}SparseMoeBlock"])
-    model_cls = getattr(module, f"{model_cls_prefix}SparseMoeBlock")
-    return model_cls

src/axolotl/integrations/llm_compressor/utils.py

@@ -12,6 +12,7 @@ def save_compressed_model(
     model: PreTrainedModel,
     output_dir: Union[str, bytes],
     trainer: Trainer,
+    safe_serialization: bool = False,
     save_compressed: bool = False,
 ) -> None:
     """
@@ -21,6 +22,7 @@ def save_compressed_model(
         model (PreTrainedModel): The model to be saved.
         output_dir (str or bytes): Path where the model files will be written.
         trainer (Trainer): Hugging Face Trainer for process synchronization.
+        safe_serialization (bool): Use safe serialization if True.
         save_compressed (bool): Write compressed tensors if True.
     """
     trainer.accelerator.wait_for_everyone()
@@ -32,6 +34,7 @@ def save_compressed_model(
     modify_save_pretrained(model)
     model.save_pretrained(
         output_dir,
+        safe_serialization=safe_serialization,
         save_compressed=save_compressed,
         skip_sparsity_compression_stats=not save_compressed,
     )

src/axolotl/loaders/model.py

@@ -26,6 +26,7 @@ from torch.distributed import DeviceMesh
 from transformers import (
     AutoModelForCausalLM,
     AutoModelForImageTextToText,
+    AutoModelForVision2Seq,
     AwqConfig,
     BitsAndBytesConfig,
     GPTQConfig,
@@ -225,7 +226,6 @@ class ModelLoader:
         ):
             self.model = self.model.merge_and_unload()
 
-        self._configure_experts_implementation()
         self._apply_activation_checkpointing()
         self._resize_token_embeddings()
         self._adjust_model_config()
@@ -233,10 +233,6 @@ class ModelLoader:
         self._configure_qat()
         log_gpu_memory_usage(LOG, "Memory usage after model load", 0)
 
-    def _configure_experts_implementation(self):
-        if self.cfg.experts_implementation is not None:
-            self.model.set_experts_implementation(self.cfg.experts_implementation)
-
     def _apply_activation_checkpointing(self):
         if self.cfg.activation_offloading is True:
             from axolotl.core.trainers.mixins.activation_checkpointing import (
@@ -438,7 +434,7 @@ class ModelLoader:
         """
         if self.cfg.is_multimodal:
             self.auto_model_loader = MULTIMODAL_AUTO_MODEL_MAPPING.get(
-                self.model_config.model_type, AutoModelForImageTextToText
+                self.model_config.model_type, AutoModelForVision2Seq
             )
             if isinstance(self.auto_model_loader, str):
                 self.auto_model_loader = AutoModelForImageTextToText
@@ -480,7 +476,6 @@ class ModelLoader:
             max_memory = None
 
         self.model_kwargs["torch_dtype"] = self.cfg.torch_dtype
-        self.model_kwargs["dtype"] = self.cfg.torch_dtype
 
         is_ds_zero3 = is_deepspeed_zero3_enabled()
 
@@ -675,7 +670,7 @@ class ModelLoader:
         Uses the selected loader when provided; otherwise falls back to the auto loader.
         """
         loader = model_loader_class or self.auto_model_loader
-        if loader in [AutoModelForCausalLM, AutoModelForImageTextToText]:
+        if loader in [AutoModelForCausalLM, AutoModelForVision2Seq]:
             model = loader.from_config(
                 config=self.model_config,
                 trust_remote_code=self.cfg.trust_remote_code or False,
@@ -793,7 +788,6 @@ class ModelLoader:
                 # Use auto model loader (handles gptq and default cases)
                 model_loader_class = self.auto_model_loader
 
-            self.model_kwargs["dtype"] = self.model_kwargs["torch_dtype"]
             if self.cfg.reinit_weights:
                 self.model = self._load_model_from_config(model_loader_class)
             else:

src/axolotl/loaders/patch_manager.py

@@ -220,6 +220,13 @@ class PatchManager:
 
             patch_qwen3_next_modeling_packing()
 
+        if self.cfg.model_config_type == "mistral3" and self.cfg.processor_type:
+            from axolotl.monkeypatch.models.mistral3.mistral_common_tokenizer import (
+                apply_mistral_tokenizer_image_patch,
+            )
+
+            apply_mistral_tokenizer_image_patch()
+
         if self.cfg.model_config_type == "kimi_linear":
             from axolotl.monkeypatch.models.kimi_linear.patch_kimi_linear import (
                 patch_kimi_model,

src/axolotl/loaders/processor.py

@@ -31,7 +31,7 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
 
             from axolotl.utils.mistral import HFMistralTokenizer
 
-            tokenization_mistral_common.MistralCommonBackend = HFMistralTokenizer
+            tokenization_mistral_common.MistralCommonTokenizer = HFMistralTokenizer
 
         _patch_mistralcommontokenizer()
 

src/axolotl/models/mamba/modeling_mamba.py

@@ -111,6 +111,7 @@ class MambaLMHeadModel(nn.Module, GenerationMixin):
         self,
         save_directory: Union[str, os.PathLike],
         state_dict: Optional[dict] = None,
+        safe_serialization: Optional[bool] = None,
     ):
         if state_dict is None:
             state_dict = self.state_dict()

src/axolotl/monkeypatch/loss/eaft.py

@@ -1,51 +0,0 @@
-"""
-eaft (entropy-aware focal training) loss implementation
-weights examples by entropy approximation from top-k logits
-
-Reference: https://github.com/ymxyll/LlamaFactory-EAFT/blob/e2ce19e8efcc226450ee8f2b81dfe4e69f1f945d/src/llamafactory/train/trainer_utils.py
-"""
-
-import torch
-import torch.nn.functional as F
-
-
-def eaft_loss(outputs, labels, num_items_in_batch=None, alpha=1.0, k=20):
-    """
-    compute eaft loss with entropy weighting
-
-    args:
-        outputs: model outputs containing logits
-        labels: target labels for computing loss
-        num_items_in_batch: for sample packing support
-        alpha: exponent for entropy weighting (default 1.0)
-        k: number of top logits for entropy approximation (default 20)
-    """
-    logits = outputs.logits
-
-    shift_logits = logits[..., :-1, :].contiguous()
-    shift_labels = labels[..., 1:].contiguous()
-
-    vocab_size = shift_logits.size(-1)
-    shift_logits_view = shift_logits.view(-1, vocab_size)
-    shift_labels_view = shift_labels.view(-1)
-
-    mask = shift_labels_view != -100
-
-    with torch.no_grad():
-        top_k_logits, _ = torch.topk(
-            shift_logits_view[mask].float(), k=min(k, vocab_size), dim=-1
-        )
-        top_k_probs = F.softmax(top_k_logits, dim=-1)
-        entropy = -(top_k_probs * torch.log(top_k_probs + 1e-10)).sum(dim=-1)
-        weights = torch.pow(entropy, alpha)
-
-    loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
-    per_token_loss = loss_fct(shift_logits_view[mask], shift_labels_view[mask])
-    weighted_loss = per_token_loss * weights
-
-    if num_items_in_batch is not None:
-        loss = weighted_loss.sum() / num_items_in_batch
-    else:
-        loss = weighted_loss.mean()
-
-    return loss

src/axolotl/monkeypatch/models/mistral3/mistral_common_tokenizer.py

@@ -1,5 +1,5 @@
 """
-Monkeypatch to fix inefficient tensor conversion in MistralCommonBackend.apply_chat_template
+Monkeypatch to fix inefficient tensor conversion in MistralCommonTokenizer.apply_chat_template
 """
 
 import importlib
@@ -12,11 +12,11 @@ LOG = get_logger(__name__)
 
 
 def apply_mistral_tokenizer_image_patch():
-    """Apply patch to MistralCommonBackend.apply_chat_template to fix image tensor conversion."""
-    from transformers.tokenization_mistral_common import MistralCommonBackend
+    """Apply patch to MistralCommonTokenizer.apply_chat_template to fix image tensor conversion."""
+    from transformers.tokenization_mistral_common import MistralCommonTokenizer
 
     # Get original source
-    original_source = inspect.getsource(MistralCommonBackend.apply_chat_template)
+    original_source = inspect.getsource(MistralCommonTokenizer.apply_chat_template)
     original_source, _ = detab_code(original_source)
 
     # Define the replacement
@@ -41,7 +41,7 @@ def apply_mistral_tokenizer_image_patch():
         )
 
         # Load necessary imports from the module
-        module_name = MistralCommonBackend.__module__
+        module_name = MistralCommonTokenizer.__module__
         module = importlib.import_module(module_name)
 
         # Detect what needs to be imported
@@ -79,7 +79,7 @@ def apply_mistral_tokenizer_image_patch():
         exec(patched_source, globals())  # nosec B102
 
         # Replace the method
-        MistralCommonBackend.apply_chat_template = patched_apply_chat_template
-        LOG.info("Successfully applied MistralCommonBackend tensor conversion patch")
+        MistralCommonTokenizer.apply_chat_template = patched_apply_chat_template
+        LOG.info("Successfully applied MistralCommonTokenizer tensor conversion patch")
     else:
-        LOG.warning("Could not find target code for MistralCommonBackend patching")
+        LOG.warning("Could not find target code for MistralCommonTokenizer patching")

src/axolotl/monkeypatch/relora.py

@@ -155,6 +155,7 @@ class ReLoRACallback(TrainerCallback):
                     f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
                     "adapter",
                 ),
+                safe_serialization=True,
             )
             with torch.no_grad():
                 merge_and_save(
@@ -213,7 +214,7 @@ class ReLoRACallback(TrainerCallback):
 
                     self.last_full_model = checkpoint_folder
             else:
-                model.model.save_pretrained(checkpoint_folder)
+                model.model.save_pretrained(checkpoint_folder, safe_serialization=True)
 
         return control
 

src/axolotl/monkeypatch/transformers/trainer_context_parallel.py

@@ -52,15 +52,9 @@ def patch_prepare_context_parallel_inputs() -> None:
         if item in patched_source:
             items_to_import.append(item)
 
-    # Use a separate namespace to capture the exec'd function
-    namespace = {}
-    exec(f"from {module_name} import ({', '.join(items_to_import)})", namespace)
-    exec(patched_source, namespace)
+    exec(f"from {module_name} import ({', '.join(items_to_import)})", globals())
+    exec(patched_source, globals())
 
-    # Explicitly get the function from the namespace
-    axolotl_prepare_context_parallel_inputs = namespace[
-        "axolotl_prepare_context_parallel_inputs"
-    ]
     Trainer._original_prepare_context_parallel_inputs = (
         Trainer._prepare_context_parallel_inputs
     )

src/axolotl/processing_strategies.py

@@ -14,6 +14,7 @@ from transformers.models.voxtral import VoxtralProcessor
 
 from axolotl.utils.dict import remove_none_values
 from axolotl.utils.logging import get_logger
+from axolotl.utils.mistral.mistral3_processor import Mistral3Processor
 
 LOG = get_logger(__name__)
 
@@ -429,7 +430,7 @@ class Mistral3ProcessingStrategy(ProcessingStrategy):
 
     def __init__(
         self,
-        processor,
+        processor: Mistral3Processor,
         chat_template: Optional[str] = None,
         image_size: int | tuple[int, int] | None = None,
         image_resize_algorithm: Resampling | None = None,
@@ -492,8 +493,6 @@ def get_processing_strategy(
     image_size: int | tuple[int, int] | None = None,
     image_resize_algorithm: Resampling | None = None,
 ):
-    from axolotl.utils.mistral.mistral3_processor import Mistral3Processor
-
     processing_kwargs = {
         "processor": processor,
         "chat_template": chat_template,

src/axolotl/prompt_strategies/chat_template.py

@@ -150,8 +150,6 @@ class ChatTemplatePrompter(Prompter):
 
         return self.tokenizer.apply_chat_template(
             conversation,
-            tokenize=True,
-            return_dict=False,
             **chat_template_kwargs,
         )
 

src/axolotl/train.py

@@ -135,13 +135,16 @@ def setup_reference_model(
     return model_ref
 
 
-def setup_signal_handler(cfg: DictDefault, model: PreTrainedModel):
+def setup_signal_handler(
+    cfg: DictDefault, model: PreTrainedModel, safe_serialization: bool
+):
     """
     Set up signal handler for graceful termination.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
         model: The model to save on termination
+        safe_serialization: Whether to use safe serialization when saving
     """
     # ray workers don't have access to this signal
     if cfg.local_rank == 0 and not cfg.use_ray:
@@ -149,7 +152,9 @@ def setup_signal_handler(cfg: DictDefault, model: PreTrainedModel):
         def terminate_handler(_, __, model_weakref):
             if model_weakref() is not None:
                 _model = model_weakref()
-                _model.save_pretrained(cfg.output_dir)
+                _model.save_pretrained(
+                    cfg.output_dir, safe_serialization=safe_serialization
+                )
 
             cleanup_distributed()
             sys.exit(0)
@@ -214,6 +219,7 @@ def save_trained_model(
     cfg: DictDefault,
     trainer: Any,
     model: PreTrainedModel,
+    safe_serialization: bool,
 ):
     """
     Save the trained model according to configuration and training setup.
@@ -222,6 +228,7 @@ def save_trained_model(
         cfg: Dictionary mapping `axolotl` config keys to values.
         trainer: The trainer object.
         model: The trained model to save.
+        safe_serialization: Whether to use safe serialization.
     """
     LOG.info(f"Training completed! Saving trained model to {cfg.output_dir}.")
 
@@ -276,6 +283,7 @@ def save_trained_model(
                 merge_fsdp_weights(
                     checkpoint_dir=str(fsdp_dir),
                     output_path=merged_path,
+                    safe_serialization=True,
                 )
                 trainer.accelerator.wait_for_everyone()
                 if trainer.accelerator.is_main_process:
@@ -322,9 +330,11 @@ def save_trained_model(
                 pass
     elif cfg.local_rank == 0:
         if cfg.rl and cfg.adapter and not cfg.rl_adapter_ref_model:
-            trainer.model.save_pretrained(cfg.output_dir)
+            trainer.model.save_pretrained(
+                cfg.output_dir, safe_serialization=safe_serialization
+            )
 
-        model.save_pretrained(cfg.output_dir)
+        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
     if hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
         # TODO: add integration support so this can be implemented completely within the plugin
@@ -334,6 +344,7 @@ def save_trained_model(
             model=model,
             output_dir=cfg.output_dir,
             trainer=trainer,
+            safe_serialization=safe_serialization,
             save_compressed=cfg.llmcompressor.save_compressed,
         )
 
@@ -438,6 +449,7 @@ def handle_untrained_tokens_fix(
     model: PreTrainedModel,
     tokenizer: PreTrainedTokenizer,
     train_dataset: Dataset,
+    safe_serialization: bool,
 ):
     """
     Apply fixes for untrained tokens if configured.
@@ -447,6 +459,7 @@ def handle_untrained_tokens_fix(
         model: The model to apply fixes to.
         tokenizer: The tokenizer for token identification.
         train_dataset: The training dataset to use.
+        safe_serialization: Whether to use safe serialization when saving.
     """
     if not cfg.fix_untrained_tokens:
         return
@@ -470,7 +483,9 @@ def handle_untrained_tokens_fix(
     fix_untrained_tokens(model, tokenizer, train_dataset, **fix_kwargs)
 
     if cfg.local_rank == 0:
-        model.save_pretrained(str(Path(cfg.output_dir)))
+        model.save_pretrained(
+            str(Path(cfg.output_dir)), safe_serialization=safe_serialization
+        )
 
 
 def setup_model_and_trainer(
@@ -567,12 +582,15 @@ def train(
     ) = setup_model_and_trainer(cfg, dataset_meta)
 
     # Handle untrained tokens if configured
+    safe_serialization = cfg.save_safetensors is True
     train_dataset = dataset_meta.train_dataset
-    handle_untrained_tokens_fix(cfg, model, tokenizer, train_dataset)
+    handle_untrained_tokens_fix(
+        cfg, model, tokenizer, train_dataset, safe_serialization
+    )
 
     # Additional setup
     save_initial_configs(cfg, tokenizer, model, peft_config, processor)
-    setup_signal_handler(cfg, model)
+    setup_signal_handler(cfg, model, safe_serialization)
     setup_model_card(cfg)
 
     # Execute the training
@@ -584,7 +602,7 @@ def train(
         torch.cuda.empty_cache()
 
     # Save the trained model and cleanup
-    save_trained_model(cfg, trainer, model)
+    save_trained_model(cfg, trainer, model, safe_serialization)
     tokenizer.save_pretrained(
         str(Path(cfg.output_dir)), save_jinja_files=cfg.tokenizer_save_jinja_files
     )

src/axolotl/utils/callbacks/perplexity.py

@@ -7,11 +7,7 @@ from torch import Tensor
 from tqdm import tqdm
 from transformers.modeling_outputs import CausalLMOutput
 from transformers.modeling_utils import PreTrainedModel
-
-try:
-    from transformers.tokenization_python import PreTrainedTokenizer
-except ImportError:
-    from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.tokenization_utils import PreTrainedTokenizer
 
 from axolotl.utils.distributed import is_main_process
 

src/axolotl/utils/data/rl.py

@@ -173,7 +173,7 @@ def _drop_long_sequences(
 
         return (len_prompt + len_completion) <= sequence_len
 
-    if rl in {RLType.GRPO, RLType.GDPO}:
+    if rl is RLType.GRPO:
         return True
 
     raise ValueError("Unknown RL type")

src/axolotl/utils/mistral/mistral_tokenizer.py

@@ -7,11 +7,11 @@ import numpy as np
 from mistral_common.protocol.instruct.validator import ValidationMode
 from mistral_common.tokens.tokenizers.utils import download_tokenizer_from_hf_hub
 from torch import Tensor
-from transformers.tokenization_mistral_common import MistralCommonBackend
+from transformers.tokenization_mistral_common import MistralCommonTokenizer
 from transformers.tokenization_utils_base import VERY_LARGE_INTEGER
 
 
-class HFMistralTokenizer(MistralCommonBackend):
+class HFMistralTokenizer(MistralCommonTokenizer):
     """
     Wraps mistral_common.tokens.tokenizers.mistral.MistralTokenizer
     and exposes HuggingFace API for special tokens.
@@ -37,19 +37,11 @@ class HFMistralTokenizer(MistralCommonBackend):
     def name_or_path(self) -> str:
         return self._name_or_path
 
-    @name_or_path.setter
-    def name_or_path(self, name_or_path: str) -> None:
-        self._name_or_path = name_or_path
-
     @property
     def chat_template(self) -> str | None:
         """Chat template is not supported. Dummy method to satisfy HuggingFace API."""
         return "[This is a dummy chat template]"
 
-    @chat_template.setter
-    def chat_template(self, chat_template: str | None) -> None:
-        pass
-
     def _set_mode(self, mode: ValidationMode):
         """Set the mode of the MistralRequestValidator.
 
@@ -141,7 +133,7 @@ class HFMistralTokenizer(MistralCommonBackend):
         r"""
         Patched fn to pass `name_or_path` and remove extra kwargs.
 
-        Instantiate a `MistralCommonBackend` from a predefined
+        Instantiate a `MistralCommonTokenizer` from a predefined
         tokenizer.
 
         Args:
@@ -150,7 +142,7 @@ class HFMistralTokenizer(MistralCommonBackend):
 
                 - A string, the *model id* of a predefined tokenizer hosted inside a model repo on huggingface.co.
                 - A path to a *directory* containing the tokenizer config, for instance saved
-                  using the [`MistralCommonBackend.tokenization_mistral_common.save_pretrained`] method, e.g.,
+                  using the [`MistralCommonTokenizer.tokenization_mistral_common.save_pretrained`] method, e.g.,
                   `./my_model_directory/`.
             mode (`ValidationMode`, *optional*, defaults to `ValidationMode.test`):
                 Validation mode for the `MistralTokenizer` tokenizer.
@@ -162,7 +154,7 @@ class HFMistralTokenizer(MistralCommonBackend):
                 exist.
             token (`str` or *bool*, *optional*):
                 The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
-                when running `hf auth login` (stored in `~/.huggingface`).
+                when running `huggingface-cli login` (stored in `~/.huggingface`).
             local_files_only (`bool`, *optional*, defaults to `False`):
                 Whether or not to only rely on local files and not to attempt to download any files.
             revision (`str`, *optional*, defaults to `"main"`):
@@ -187,12 +179,12 @@ class HFMistralTokenizer(MistralCommonBackend):
                 Whether or not the model should cleanup the spaces that were added when splitting the input text during the
                 tokenization process.
             kwargs (additional keyword arguments, *optional*):
-                Not supported by `MistralCommonBackend.from_pretrained`.
+                Not supported by `MistralCommonTokenizer.from_pretrained`.
                 Will raise an error if used.
         """
         if init_inputs:
             raise ValueError(
-                "`init_inputs` are not supported by `MistralCommonBackend.from_pretrained`."
+                "`init_inputs` are not supported by `MistralCommonTokenizer.from_pretrained`."
             )
 
         # Delete trust_remote_code as it does nothing
@@ -204,7 +196,7 @@ class HFMistralTokenizer(MistralCommonBackend):
         # Handle kwargs and AutoTokenizer case
         if kwargs and not kwargs.keys() == {"_from_auto"}:
             raise ValueError(
-                f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonBackend.from_pretrained`."
+                f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer.from_pretrained`."
             )
 
         if not os.path.isfile(pretrained_model_name_or_path):

src/axolotl/utils/schemas/config.py

@@ -619,13 +619,6 @@ class AxolotlInputConfig(
         },
     )
 
-    experts_implementation: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Which experts implementation to use for MoE models,"
-        },
-    )
-
     scaling_softmax: bool | None = Field(
         default=None,
         json_schema_extra={
@@ -683,24 +676,6 @@ class AxolotlInputConfig(
             "description": "Number of chunks to use for chunked cross entropy loss"
         },
     )
-    use_eaft: bool | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Enable Entropy-Aware Focal Training loss (EAFT)"
-        },
-    )
-    eaft_alpha: float | None = Field(
-        default=1.0,
-        json_schema_extra={
-            "description": "Exponent for entropy weighting in EAFT (default: 1.0)"
-        },
-    )
-    eaft_k: int | None = Field(
-        default=20,
-        json_schema_extra={
-            "description": "Number of top logits for entropy approximation (default: 20)"
-        },
-    )
 
     tiled_mlp: bool | None = Field(
         default=None,

src/axolotl/utils/schemas/enums.py

@@ -26,7 +26,6 @@ class RLType(str, Enum):
     """RL trainer type configuration subset"""
 
     DPO = "dpo"
-    GDPO = "gdpo"
     GRPO = "grpo"
     IPO = "ipo"
     ORPO = "orpo"

src/axolotl/utils/schemas/fsdp.py

@@ -4,7 +4,7 @@ FSDP Configuration Schema
 
 from typing import Literal
 
-from pydantic import AliasChoices, BaseModel, Field
+from pydantic import BaseModel, Field
 
 
 class FSDPConfig(BaseModel):
@@ -12,11 +12,6 @@ class FSDPConfig(BaseModel):
     FSDP Configuration Schema
     """
 
-    fsdp_version: int | None = Field(
-        validation_alias=AliasChoices("fsdp_version", "version"),
-        default=None,
-        json_schema_extra={"description": "FSDP version"},
-    )
     activation_checkpointing: bool | None = Field(
         default=None,
         description="Enable activation checkpointing to reduce memory usage during forward passes",

src/axolotl/utils/schemas/model.py

@@ -123,22 +123,10 @@ class ModelOutputConfig(BaseModel):
     save_safetensors: bool | None = Field(
         default=True,
         json_schema_extra={
-            "description": "Whether to save the model using safetensors format. Defaults to True."
+            "description": "Save model as safetensors (require safetensors package). Default True"
         },
     )
 
-    @field_validator("save_safetensors")
-    @classmethod
-    def validate_save_safetensors(cls, v):
-        if v is False:
-            raise ValueError(
-                "save_safetensors=False is not supported in Transformers V5. "
-                "Transformers V5 always uses safetensors format for model serialization. "
-                "This field is deprecated and will be removed in a future version."
-            )
-        # Allow None and True, will default to True if None
-        return True if v is None else v
-
 
 class SpecialTokensConfig(BaseModel):
     """Special tokens configuration subset"""

src/axolotl/utils/schemas/trl.py

@@ -179,13 +179,3 @@ class TRLConfig(BaseModel):
             "description": "Path to custom rollout function. Must be importable from current dir."
         },
     )
-    multi_objective_aggregation: (
-        Literal["sum_then_normalize", "normalize_then_sum"] | None
-    ) = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Multi-objective reward aggregation strategy. "
-            "'sum_then_normalize' (GRPO default): weights and sums rewards first, then normalizes. "
-            "'normalize_then_sum' (GDPO): normalizes each reward independently, then sums."
-        },
-    )

src/axolotl/utils/schemas/validation.py

@@ -746,19 +746,6 @@ class RLValidationMixin:
             )
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_gdpo(cls, data):
-        if (
-            data.get("rl") == "gdpo"
-            and data.get("trl", {}).get("multi_objective_aggregation")
-            == "sum_then_normalize"
-        ):
-            raise ValueError(
-                "`multi_objective_aggregation` value set as `sum_then_normalize` => GRPO, but GDPO was selected"
-            )
-        return data
-
 
 class OptimizationValidationMixin:
     """Validation methods related to optimization and performance."""
@@ -900,43 +887,6 @@ class OptimizationValidationMixin:
 
         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="before")
-    @classmethod
-    def check_fsdp_version_in_fsdp_config(cls, data):
-        fsdp_config = data.get("fsdp_config") or {}
-        fsdp_version = data.get("fsdp_version", None)
-        if not fsdp_version and fsdp_config and fsdp_config.get("version"):
-            fsdp_cfg_version = fsdp_config.pop("version")
-            data["fsdp_version"] = fsdp_cfg_version
-            data["fsdp_config"]["fsdp_version"] = fsdp_cfg_version
-        elif not fsdp_version and fsdp_config and fsdp_config.get("fsdp_version"):
-            data["fsdp_version"] = fsdp_config.get("fsdp_version")
-        if fsdp_version and fsdp_config and not fsdp_config.get("fsdp_version"):
-            data["fsdp_config"]["fsdp_version"] = fsdp_version
-        return data
-
     @model_validator(mode="after")
     def check_fsdp_offload_w_8bit_optimizer(self):
         if (
@@ -1038,6 +988,40 @@ 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
+
 
 class SystemValidationMixin:
     """Validation methods related to system and hardware configuration."""

tests/conftest.py

@@ -83,12 +83,6 @@ def download_smollm2_135m_model():
     snapshot_download_w_retry("HuggingFaceTB/SmolLM2-135M", repo_type="model")
 
 
-@pytest.fixture(scope="session", autouse=True)
-def download_smollm2_135m_instruct_model():
-    # download the model
-    snapshot_download_w_retry("HuggingFaceTB/SmolLM2-135M-Instruct", repo_type="model")
-
-
 @pytest.fixture(scope="session", autouse=True)
 def download_smollm2_135m_gptq_model():
     # download the model
@@ -149,20 +143,12 @@ def download_argilla_distilabel_intel_orca_dpo_dataset():
     )
 
 
-@pytest.fixture(scope="session", autouse=True)
-def download_argilla_ultrafeedback_binarized_preferences_cleaned_dataset():
-    # download the dataset
-    snapshot_download_w_retry(
-        "argilla/ultrafeedback-binarized-preferences-cleaned", repo_type="dataset"
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_argilla_ultrafeedback_binarized_preferences_cleaned_kto_dataset():
-    # download the dataset
-    snapshot_download_w_retry(
-        "argilla/ultrafeedback-binarized-preferences-cleaned-kto", repo_type="dataset"
-    )
+# @pytest.fixture(scope="session", autouse=True)
+# def download_argilla_ultrafeedback_binarized_preferences_cleaned_dataset():
+#     # download the dataset
+#     snapshot_download_w_retry(
+#         "argilla/ultrafeedback-binarized-preferences-cleaned", repo_type="dataset"
+#     )
 
 
 # @pytest.fixture(scope="session", autouse=True)
@@ -265,9 +251,7 @@ def download_llama_1b_model_fixture():
 def download_llama3_8b_model_fixture():
     # download the tokenizer only
     snapshot_download_w_retry(
-        "NousResearch/Meta-Llama-3-8B",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
+        "NousResearch/Meta-Llama-3-8B", repo_type="model", allow_patterns=["*token*"]
     )
 
 
@@ -277,7 +261,7 @@ def download_llama3_8b_instruct_model_fixture():
     snapshot_download_w_retry(
         "NousResearch/Meta-Llama-3-8B-Instruct",
         repo_type="model",
-        allow_patterns=["*token*", "config.json"],
+        allow_patterns=["*token*"],
     )
 
 
@@ -285,19 +269,7 @@ def download_llama3_8b_instruct_model_fixture():
 def download_phi_35_mini_model_fixture():
     # download the tokenizer only
     snapshot_download_w_retry(
-        "microsoft/Phi-3.5-mini-instruct",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_phi_4_reasoning_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "microsoft/Phi-4-reasoning",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
+        "microsoft/Phi-3.5-mini-instruct", repo_type="model", allow_patterns=["*token*"]
     )
 
 
@@ -307,7 +279,7 @@ def download_phi_3_medium_model_fixture():
     snapshot_download_w_retry(
         "microsoft/Phi-3-medium-128k-instruct",
         repo_type="model",
-        allow_patterns=["*token*", "config.json"],
+        allow_patterns=["*token*"],
     )
 
 
@@ -590,8 +562,6 @@ def test_load_fixtures(
     download_mhenrichsen_alpaca_2k_dataset,
     download_mhenrichsen_alpaca_2k_w_revision_dataset,
     download_mlabonne_finetome_100k_dataset,
-    download_argilla_ultrafeedback_binarized_preferences_cleaned_dataset,
-    download_argilla_ultrafeedback_binarized_preferences_cleaned_kto_dataset,
     download_argilla_distilabel_capybara_dpo_7k_binarized_dataset,
     download_arcee_ai_distilabel_intel_orca_dpo_pairs_dataset,
     download_argilla_dpo_pairs_dataset,
@@ -603,7 +573,6 @@ def test_load_fixtures(
     download_llama3_8b_instruct_model_fixture,
     download_phi_35_mini_model_fixture,
     download_phi_3_medium_model_fixture,
-    download_phi_4_reasoning_model_fixture,
     download_mistral_7b_model_fixture,
     download_gemma_2b_model_fixture,
     download_gemma2_9b_model_fixture,

tests/core/test_builders.py

@@ -53,6 +53,7 @@ def fixture_base_cfg():
             # Checkpointing and saving
             "save_steps": 100,
             "output_dir": "./model-out",
+            "save_safetensors": True,
             "save_total_limit": 4,
             "save_only_model": False,
             # Hardware/performance settings
@@ -310,6 +311,7 @@ class TestHFRLTrainerBuilder:
         # KTO specific
         assert training_arguments.desirable_weight == 1.0
         assert training_arguments.undesirable_weight == 1.0
+        assert training_arguments.max_prompt_length == 512
 
     def _write_rewards_file(self, rewards_dir: Path):
         """

tests/e2e/integrations/test_cut_cross_entropy.py

@@ -10,7 +10,7 @@ from axolotl.utils import get_pytorch_version
 from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_model_output_exists
+from ..utils import check_model_output_exists
 
 
 @pytest.fixture()
@@ -39,6 +39,7 @@ def min_cfg(temp_dir):
         "optimizer": "adamw_torch_fused",
         "output_dir": temp_dir,
         "lr_scheduler": "cosine",
+        "save_safetensors": True,
         "max_steps": 10,
         "bf16": "auto",
         "save_first_step": False,
@@ -91,6 +92,7 @@ class TestCutCrossEntropyIntegration:
                 "optimizer": "adamw_torch_fused",
                 "output_dir": temp_dir,
                 "lr_scheduler": "cosine",
+                "save_safetensors": True,
                 "max_steps": 10,
                 "bf16": "auto",
                 "save_first_step": False,

tests/e2e/integrations/test_fp8.py

@@ -48,6 +48,7 @@ class FP8IntegrationTestCase:
                 "sample_packing": True,
                 "fp8": True,
                 "torch_compile": True,
+                "save_safetensors": True,
                 "save_first_step": False,
             }
         )

tests/e2e/integrations/test_hooks.py

@@ -11,7 +11,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_model_output_exists
+from ..utils import check_model_output_exists
 
 
 class LogHooksPlugin(BasePlugin):

tests/e2e/integrations/test_kd.py

@@ -65,6 +65,7 @@ def min_cfg(temp_dir):
         },
         "max_steps": 5,
         "output_dir": temp_dir,
+        "save_safetensors": True,
         "use_tensorboard": True,
         "save_first_step": False,
     }

tests/e2e/integrations/test_liger.py

@@ -48,6 +48,7 @@ class LigerIntegrationTestCase:
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
+                "save_safetensors": True,
                 "bf16": "auto",
                 "max_steps": 5,
                 "save_first_step": False,
@@ -98,6 +99,7 @@ class LigerIntegrationTestCase:
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
+                "save_safetensors": True,
                 "bf16": "auto",
                 "max_steps": 5,
                 "save_first_step": False,

tests/e2e/integrations/test_llm_compressor.py

@@ -57,6 +57,7 @@ class TestLLMCompressorIntegration:
                 "learning_rate": 1e-5,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
+                "save_safetensors": True,
                 "bf16": "auto",
                 "max_steps": 5,
                 "llmcompressor": {

tests/e2e/multigpu/solo/test_gdpo.py

@@ -1,538 +0,0 @@
-"""
-GDPO test suite
-
-GDPO uses TRL's multi_objective_aggregation="normalize_then_sum" for
-per-reward normalization in multi-reward RL training.
-"""
-
-import os
-import random
-from pathlib import Path
-
-import pytest
-import yaml
-from accelerate.test_utils import execute_subprocess_async
-from transformers.testing_utils import get_torch_dist_unique_port
-
-from axolotl.utils.dict import DictDefault
-
-from tests.e2e.multigpu.solo.test_grpo import recursive_kill, start_vllm
-from tests.e2e.utils import require_vllm
-
-
-@pytest.mark.skip(reason="flaky vllm tests in modal")
-class TestGDPO:
-    """Test case for GDPO training using TRL's native multi-objective aggregation."""
-
-    def _utils_write_yaml_and_rewards(self, cfg, temp_dir, suffix=""):
-        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))
-        with open(f"rewards_gdpo_{suffix}.py", "w", encoding="utf-8") as fout:
-            fout.write(
-                """import random
-
-def format_reward(prompts, completions, **kwargs) -> list[float]:
-    return [1.0 if len(c) > 10 else 0.0 for c in completions]
-
-def correctness_reward(prompts, completions, **kwargs) -> list[float]:
-    return [random.uniform(-1, 3) for _ in completions]
-
-def safety_reward(prompts, completions, **kwargs) -> list[float]:
-    return [1.0 if 'error' not in c.lower() else 0.0 for c in completions]
-
-def single_reward(prompts, completions, **kwargs) -> list[float]:
-    return [random.uniform(0, 1) for _ in completions]
-
-def oai_gsm8k_transform(cfg, *args, **kwargs):
-    def transform_fn(example, tokenizer=None):
-        label = example["answer"].split("####")[-1].strip().replace(",", "")
-        return {
-            "prompt": [{"role": "user", "content": example["question"]}],
-            "answer": label,
-        }
-    return transform_fn, {"remove_columns": ["question"]}
-"""
-            )
-
-    @pytest.mark.parametrize("num_gpus", [1, 2])
-    @require_vllm
-    def test_gdpo_multi_reward_lora(self, temp_dir, num_gpus):
-        """Test GDPO with multiple reward functions using LoRA."""
-        rnd_suffix = str(random.randint(1000, 9999))
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "chat_template": "llama3",
-                "rl": "gdpo",
-                "trl": {
-                    "beta": 0.001,
-                    "max_completion_length": 256,
-                    "use_vllm": True,
-                    "num_generations": 4,
-                    "reward_funcs": [
-                        f"rewards_gdpo_{rnd_suffix}.format_reward",
-                        f"rewards_gdpo_{rnd_suffix}.correctness_reward",
-                    ],
-                    "reward_weights": [1.0, 2.0],
-                    "scale_rewards": True,
-                },
-                "vllm": {
-                    "max_model_len": 800,
-                    "enable_prefix_caching": True,
-                },
-                "datasets": [
-                    {
-                        "path": "openai/gsm8k",
-                        "name": "main",
-                        "type": f"rewards_gdpo_{rnd_suffix}.oai_gsm8k_transform",
-                    },
-                ],
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "flash_attention": True,
-                "sequence_len": 1024,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "max_steps": 3,
-                "num_epochs": 1,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 2,
-                "warmup_steps": 10,
-                "val_set_size": 0.0,
-                "output_dir": temp_dir,
-                "learning_rate": 0.0001,
-                "optimizer": "adamw_torch_fused",
-                "lr_scheduler": "cosine",
-                "save_safetensors": True,
-                "bf16": "auto",
-                "use_tensorboard": True,
-                "save_first_step": False,
-            }
-        )
-
-        self._utils_write_yaml_and_rewards(cfg, temp_dir, suffix=rnd_suffix)
-
-        current_env = os.environ.copy()
-        env = {
-            "NCCL_P2P_LEVEL": "LOC",
-            **current_env,
-            "CUDA_VISIBLE_DEVICES": "1",
-        }
-        vllm_process = start_vllm(
-            cfg.base_model,
-            env=env,
-            quiet=True,
-            wait=300,
-            gpu_memory_utilization=0.15,
-            max_model_len=cfg.vllm.max_model_len,
-            enable_prefix_caching=cfg.vllm.enable_prefix_caching,
-            host="0.0.0.0",
-            port=8000,
-        )
-
-        try:
-            execute_subprocess_async(
-                [
-                    "axolotl",
-                    "train",
-                    str(Path(temp_dir) / "config.yaml"),
-                    "--num-processes",
-                    str(num_gpus),
-                    "--main-process-port",
-                    f"{get_torch_dist_unique_port()}",
-                ],
-                env={
-                    "NCCL_P2P_LEVEL": "LOC",
-                    "NCCL_DEBUG": "INFO",
-                    **current_env,
-                },
-            )
-        finally:
-            recursive_kill(vllm_process)
-
-    @require_vllm
-    def test_gdpo_three_rewards(self, temp_dir):
-        """Test GDPO with three reward functions (format, correctness, safety)."""
-        rnd_suffix = str(random.randint(1000, 9999))
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "chat_template": "llama3",
-                "rl": "gdpo",
-                "trl": {
-                    "beta": 0.001,
-                    "max_completion_length": 256,
-                    "use_vllm": True,
-                    "num_generations": 4,
-                    "reward_funcs": [
-                        f"rewards_gdpo_{rnd_suffix}.format_reward",
-                        f"rewards_gdpo_{rnd_suffix}.correctness_reward",
-                        f"rewards_gdpo_{rnd_suffix}.safety_reward",
-                    ],
-                    "reward_weights": [1.0, 2.0, 1.5],
-                },
-                "vllm": {
-                    "max_model_len": 800,
-                    "enable_prefix_caching": True,
-                },
-                "datasets": [
-                    {
-                        "path": "openai/gsm8k",
-                        "name": "main",
-                        "type": f"rewards_gdpo_{rnd_suffix}.oai_gsm8k_transform",
-                    },
-                ],
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "flash_attention": True,
-                "sequence_len": 1024,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "max_steps": 3,
-                "num_epochs": 1,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 2,
-                "warmup_steps": 10,
-                "val_set_size": 0.0,
-                "output_dir": temp_dir,
-                "learning_rate": 0.0001,
-                "optimizer": "adamw_torch_fused",
-                "lr_scheduler": "cosine",
-                "save_safetensors": True,
-                "bf16": "auto",
-            }
-        )
-
-        self._utils_write_yaml_and_rewards(cfg, temp_dir, suffix=rnd_suffix)
-
-        current_env = os.environ.copy()
-        env = {
-            "NCCL_P2P_LEVEL": "LOC",
-            **current_env,
-            "CUDA_VISIBLE_DEVICES": "1",
-        }
-        vllm_process = start_vllm(
-            cfg.base_model,
-            env=env,
-            quiet=True,
-            wait=300,
-            gpu_memory_utilization=0.15,
-            max_model_len=cfg.vllm.max_model_len,
-            enable_prefix_caching=cfg.vllm.enable_prefix_caching,
-            host="0.0.0.0",
-            port=8000,
-        )
-
-        try:
-            execute_subprocess_async(
-                [
-                    "axolotl",
-                    "train",
-                    str(Path(temp_dir) / "config.yaml"),
-                    "--num-processes",
-                    "1",
-                    "--main-process-port",
-                    f"{get_torch_dist_unique_port()}",
-                ],
-                env={
-                    "NCCL_P2P_LEVEL": "LOC",
-                    "NCCL_DEBUG": "INFO",
-                    **current_env,
-                },
-            )
-        finally:
-            recursive_kill(vllm_process)
-
-    @require_vllm
-    def test_gdpo_single_reward_fallback(self, temp_dir):
-        """Test GDPO with single reward."""
-        rnd_suffix = str(random.randint(1000, 9999))
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "chat_template": "llama3",
-                "rl": "gdpo",
-                "trl": {
-                    "beta": 0.001,
-                    "max_completion_length": 256,
-                    "use_vllm": True,
-                    "num_generations": 4,
-                    "reward_funcs": [
-                        f"rewards_gdpo_{rnd_suffix}.single_reward",
-                    ],
-                    "reward_weights": [1.0],
-                },
-                "vllm": {
-                    "max_model_len": 800,
-                    "enable_prefix_caching": True,
-                },
-                "datasets": [
-                    {
-                        "path": "openai/gsm8k",
-                        "name": "main",
-                        "type": f"rewards_gdpo_{rnd_suffix}.oai_gsm8k_transform",
-                    },
-                ],
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "flash_attention": True,
-                "sequence_len": 1024,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "max_steps": 3,
-                "num_epochs": 1,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 2,
-                "warmup_steps": 10,
-                "val_set_size": 0.0,
-                "output_dir": temp_dir,
-                "learning_rate": 0.0001,
-                "optimizer": "adamw_torch_fused",
-                "lr_scheduler": "cosine",
-                "save_safetensors": True,
-                "bf16": "auto",
-            }
-        )
-
-        self._utils_write_yaml_and_rewards(cfg, temp_dir, suffix=rnd_suffix)
-
-        current_env = os.environ.copy()
-        env = {
-            "NCCL_P2P_LEVEL": "LOC",
-            **current_env,
-            "CUDA_VISIBLE_DEVICES": "1",
-        }
-        vllm_process = start_vllm(
-            cfg.base_model,
-            env=env,
-            quiet=True,
-            wait=300,
-            gpu_memory_utilization=0.15,
-            max_model_len=cfg.vllm.max_model_len,
-            enable_prefix_caching=cfg.vllm.enable_prefix_caching,
-            host="0.0.0.0",
-            port=8000,
-        )
-
-        try:
-            execute_subprocess_async(
-                [
-                    "axolotl",
-                    "train",
-                    str(Path(temp_dir) / "config.yaml"),
-                    "--num-processes",
-                    "1",
-                    "--main-process-port",
-                    f"{get_torch_dist_unique_port()}",
-                ],
-                env={
-                    "NCCL_P2P_LEVEL": "LOC",
-                    "NCCL_DEBUG": "INFO",
-                    **current_env,
-                },
-            )
-        finally:
-            recursive_kill(vllm_process)
-
-    @require_vllm
-    def test_gdpo_fft(self, temp_dir):
-        """Test GDPO with full fine-tuning (no adapter)."""
-        rnd_suffix = str(random.randint(1000, 9999))
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "chat_template": "llama3",
-                "rl": "gdpo",
-                "trl": {
-                    "beta": 0.001,
-                    "max_completion_length": 256,
-                    "use_vllm": True,
-                    "num_generations": 4,
-                    "reward_funcs": [
-                        f"rewards_gdpo_{rnd_suffix}.format_reward",
-                        f"rewards_gdpo_{rnd_suffix}.correctness_reward",
-                    ],
-                    "reward_weights": [1.0, 2.0],
-                },
-                "vllm": {
-                    "max_model_len": 800,
-                    "enable_prefix_caching": True,
-                },
-                "datasets": [
-                    {
-                        "path": "openai/gsm8k",
-                        "name": "main",
-                        "type": f"rewards_gdpo_{rnd_suffix}.oai_gsm8k_transform",
-                    },
-                ],
-                # No adapter - full fine-tuning
-                "flash_attention": True,
-                "sequence_len": 1024,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "max_steps": 3,
-                "num_epochs": 1,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 2,
-                "warmup_steps": 10,
-                "val_set_size": 0.0,
-                "output_dir": temp_dir,
-                "learning_rate": 0.0001,
-                "optimizer": "adamw_torch_fused",
-                "lr_scheduler": "cosine",
-                "save_safetensors": True,
-                "bf16": "auto",
-            }
-        )
-
-        self._utils_write_yaml_and_rewards(cfg, temp_dir, suffix=rnd_suffix)
-
-        current_env = os.environ.copy()
-        env = {
-            "NCCL_P2P_LEVEL": "LOC",
-            **current_env,
-            "CUDA_VISIBLE_DEVICES": "1",
-        }
-        vllm_process = start_vllm(
-            cfg.base_model,
-            env=env,
-            quiet=True,
-            wait=300,
-            gpu_memory_utilization=0.15,
-            max_model_len=cfg.vllm.max_model_len,
-            enable_prefix_caching=cfg.vllm.enable_prefix_caching,
-            host="0.0.0.0",
-            port=8000,
-        )
-
-        try:
-            execute_subprocess_async(
-                [
-                    "axolotl",
-                    "train",
-                    str(Path(temp_dir) / "config.yaml"),
-                    "--num-processes",
-                    "1",
-                    "--main-process-port",
-                    f"{get_torch_dist_unique_port()}",
-                ],
-                env={
-                    "NCCL_P2P_LEVEL": "LOC",
-                    "NCCL_DEBUG": "INFO",
-                    **current_env,
-                },
-            )
-        finally:
-            recursive_kill(vllm_process)
-
-    @require_vllm
-    def test_gdpo_sequence_parallel(self, temp_dir):
-        """Test GDPO with sequence parallelism."""
-        rnd_suffix = str(random.randint(1000, 9999))
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "chat_template": "llama3",
-                "rl": "gdpo",
-                "context_parallel_size": 2,
-                "trl": {
-                    "beta": 0.001,
-                    "max_completion_length": 256,
-                    "use_vllm": True,
-                    "num_generations": 4,
-                    "reward_funcs": [
-                        f"rewards_gdpo_{rnd_suffix}.format_reward",
-                        f"rewards_gdpo_{rnd_suffix}.correctness_reward",
-                    ],
-                    "reward_weights": [1.0, 2.0],
-                },
-                "vllm": {
-                    "max_model_len": 800,
-                    "enable_prefix_caching": True,
-                },
-                "datasets": [
-                    {
-                        "path": "openai/gsm8k",
-                        "name": "main",
-                        "type": f"rewards_gdpo_{rnd_suffix}.oai_gsm8k_transform",
-                    },
-                ],
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "flash_attention": True,
-                "sequence_len": 1024,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "max_steps": 3,
-                "num_epochs": 1,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 2,
-                "warmup_steps": 10,
-                "val_set_size": 0.0,
-                "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
-                "learning_rate": 0.0001,
-                "optimizer": "adamw_torch_fused",
-                "lr_scheduler": "cosine",
-                "save_safetensors": True,
-                "bf16": "auto",
-            }
-        )
-
-        self._utils_write_yaml_and_rewards(cfg, temp_dir, suffix=rnd_suffix)
-
-        current_env = os.environ.copy()
-        env = {
-            "NCCL_P2P_LEVEL": "LOC",
-            **current_env,
-            "CUDA_VISIBLE_DEVICES": "1",
-        }
-        vllm_process = start_vllm(
-            cfg.base_model,
-            env=env,
-            quiet=True,
-            wait=300,
-            gpu_memory_utilization=0.15,
-            max_model_len=cfg.vllm.max_model_len,
-            enable_prefix_caching=cfg.vllm.enable_prefix_caching,
-            host="0.0.0.0",
-            port=8000,
-        )
-
-        try:
-            execute_subprocess_async(
-                [
-                    "axolotl",
-                    "train",
-                    str(Path(temp_dir) / "config.yaml"),
-                    "--num-processes",
-                    "2",
-                    "--main-process-port",
-                    f"{get_torch_dist_unique_port()}",
-                ],
-                env={
-                    "NCCL_P2P_LEVEL": "LOC",
-                    "NCCL_DEBUG": "INFO",
-                    **current_env,
-                },
-            )
-        finally:
-            recursive_kill(vllm_process)

tests/e2e/multigpu/solo/test_grpo.py

@@ -220,6 +220,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                 "learning_rate": 0.0001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
+                "save_safetensors": True,
                 "bf16": "auto",
                 "use_tensorboard": True,
                 "save_first_step": False,
@@ -314,6 +315,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                 "learning_rate": 0.0001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
+                "save_safetensors": True,
                 "bf16": "auto",
                 "use_tensorboard": True,
                 "save_first_step": False,
@@ -406,6 +408,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                 "learning_rate": 0.0001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
+                "save_safetensors": True,
                 "bf16": "auto",
                 "use_tensorboard": True,
                 "save_first_step": False,

tests/e2e/multigpu/test_fp8_fsdp2.py

@@ -11,7 +11,7 @@ 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, supports_fp8
+from tests.e2e.utils import most_recent_subdir, require_hopper, require_torch_2_7_0
 
 AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
 
@@ -49,7 +49,7 @@ class TestFP8FSDP2:
     """Test class for FP8 mixed precision with FSDP2 functionality."""
 
     @require_torch_2_7_0
-    @supports_fp8
+    @require_hopper
     def test_fp8_fsdp2_smoke(self, temp_dir):
         """Smoke test for 2-GPU FP8 + torch.compile + FSDP2 training"""
         cfg = DictDefault(
@@ -94,6 +94,7 @@ class TestFP8FSDP2:
                     "reshard_after_forward": True,
                 },
                 "use_tensorboard": True,
+                "save_safetensors": True,
                 "save_first_step": False,
             }
         )

tests/e2e/multigpu/test_fsdp1.py

@@ -244,7 +244,6 @@ class TestFSDP1:
 
         verify_training_success(temp_dir)
 
-    @pytest.mark.skip("broken in transformers v5")
     @pytest.mark.parametrize(
         "adapter_config",
         [

tests/e2e/multigpu/test_fsdp2.py

@@ -150,10 +150,6 @@ class TestFSDP2:
                 },
                 "use_tensorboard": True,
                 "bf16": True,
-                # explicitly disable LORA kernels, as they may be auto-enabled
-                "lora_mlp_kernel": False,
-                "lora_qkv_kernel": False,
-                "lora_o_kernel": False,
             }
         )
 

tests/e2e/multigpu/test_gemma3.py

@@ -23,7 +23,6 @@ def download_model():
     snapshot_download("axolotl-mirrors/gemma-3-4b-pt", repo_type="model")
 
 
-@pytest.mark.skip(reason="FIXME")
 class TestMultiGPUGemma3:
     """
     Test case for Gemma3 models using LoRA
@@ -33,7 +32,6 @@ class TestMultiGPUGemma3:
         cfg = DictDefault(
             {
                 "base_model": "axolotl-mirrors/gemma-3-4b-pt",
-                "unfrozen_parameters": ["model.language_model.*", "lm_head"],
                 "sequence_len": 2048,
                 "ddp_find_unused_parameters": True,
                 "sample_packing": True,

tests/e2e/multigpu/test_llama.py

@@ -901,6 +901,7 @@ class TestMultiGPULlama:
                 "flash_attention": True,
                 "sample_packing": True,
                 "bf16": True,
+                "save_safetensors": True,
                 # "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero1.json"),
                 "use_tensorboard": True,
                 "save_first_step": False,

tests/e2e/patched/test_activation_checkpointing.py

@@ -66,6 +66,7 @@ class TestActivationCheckpointing:
                 "flash_attention": True,
                 "sample_packing": True,
                 "bf16": True,
+                "save_safetensors": True,
                 "gradient_checkpointing": gradient_checkpointing,
                 "save_first_step": False,
                 "dataset_num_proc": 4,

tests/e2e/patched/test_peft_embeddings.py

@@ -46,6 +46,7 @@ class TestLlamaPeftEmbeddings:
                 "flash_attention": True,
                 "sample_packing": False,
                 "bf16": "auto",
+                "save_safetensors": True,
                 "embeddings_skip_upcast": True,
                 "save_first_step": False,
             }

tests/e2e/patched/test_resume.py

@@ -58,6 +58,7 @@ class TestResumeLlama:
                 "save_total_limit": 5,
                 "max_steps": 15,
                 "use_tensorboard": True,
+                "save_safetensors": True,
                 "save_first_step": False,
                 "include_tkps": True,
             }

tests/e2e/solo/test_relora_llama.py

@@ -63,6 +63,7 @@ class TestReLoraLlama(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
+                "save_safetensors": True,
                 "use_tensorboard": True,
                 "save_first_step": False,
             }

tests/e2e/test_activation_offloading.py

@@ -57,6 +57,7 @@ class TestActivationOffloading:
                 "flash_attention": True,
                 "sample_packing": True,
                 "bf16": "auto",
+                "save_safetensors": True,
                 "gradient_checkpointing": True,
                 "activation_offloading": True,
                 "save_first_step": False,