remove monkeypatch

.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: vllm
-            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

@@ -35,26 +35,21 @@ 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,vllm"
-            num_gpus: 2
-            dockerfile: "Dockerfile-uv.jinja"
+            nightly_build: "true"
           - cuda: 130
             cuda_version: 13.0.0
             python_version: "3.11"
             pytorch: 2.9.1
-            axolotl_extras:
-#            axolotl_extras: fbgemm-gpu
+            axolotl_extras: fbgemm-gpu
             num_gpus: 2
+            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:
@@ -76,8 +71,8 @@ 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
 
@@ -48,9 +48,9 @@ jobs:
         id: tag
         run: echo ::set-output name=TAG_NAME::$(echo $GITHUB_REF | cut -d / -f 3)
 
-      - name: Update version in VERSION file
+      - name: Update version in setup.py
         run: |
-          echo "${{ steps.tag.outputs.TAG_NAME }}" | sed 's/^v//' > VERSION
+          sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
 
       - name: Build a source dist
         run: |

.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: |
@@ -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: |
@@ -264,12 +254,12 @@ jobs:
       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: vllm
+            axolotl_extras:
             dockerfile: "Dockerfile-uv.jinja"
     steps:
       - name: Checkout
@@ -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:

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 +0,0 @@
-0.13.2

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)

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/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/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/gemma3/gemma-3-1b-fft-dft.yml

@@ -0,0 +1,53 @@
+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>
+datasets:
+  - path: cgato/SlimOrcaDedupCleaned
+    type: chat_template
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+val_set_size: 0.05
+output_dir: ./outputs/gemma-3-1b-fft-dft
+
+sequence_len: 2048
+
+use_dynamic_finetuning: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 5e-5
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 2
+saves_per_epoch: 1
+weight_decay: 0.0

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/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/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/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/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/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]
@@ -24,9 +24,6 @@ Repository = "https://github.com/axolotl-ai-cloud/axolotl.git"
 py-modules = ["setuptools_axolotl_dynamic_dependencies"]
 include-package-data = true
 
-[tool.setuptools.dynamic]
-version = { file = "VERSION" }
-
 [tool.setuptools.cmdclass]
 build_py = "setuptools_axolotl_dynamic_dependencies.BuildPyCommand"
 

requirements.txt

@@ -8,16 +8,16 @@ xformers>=0.0.23.post1
 liger-kernel==0.6.4
 # END section
 
-packaging==26.0
+packaging==23.2
 
 huggingface_hub>=0.36.0
-peft>=0.18.1
+peft>=0.18.0
 tokenizers>=0.22.1
-transformers==4.57.6
+transformers==4.57.1
 accelerate==1.12.0
-datasets==4.5.0
+datasets==4.4.2
 deepspeed>=0.18.3
-trl==0.27.0
+trl==0.25.1
 hf_xet==1.2.0
 kernels==0.11.5
 trackio>=0.13.0
@@ -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

setup.py

@@ -1,5 +1,6 @@
 """setup.py for axolotl"""
 
+import ast
 import os
 import platform
 import re
@@ -25,7 +26,6 @@ def parse_requirements(extras_require_map):
                 _install_requires.append(line)
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
-        install_xformers = platform.machine() != "aarch64"
         if "Darwin" in platform.system():
             # skip packages not compatible with OSX
             skip_packages = [
@@ -62,68 +62,44 @@ def parse_requirements(extras_require_map):
             else:
                 raise ValueError("Invalid version format")
 
-            torch_parts = torch_version.split("+")
-            if len(torch_parts) == 2:
-                torch_cuda_version = torch_parts[1]
-                _dependency_links.append(
-                    f"https://download.pytorch.org/whl/{torch_cuda_version}"
-                )
-
             if (major, minor) >= (2, 9):
                 extras_require_map.pop("fbgemm-gpu")
-                extras_require_map["fbgemm-gpu"] = [
-                    "fbgemm-gpu==1.4.0",
-                    "fbgemm-gpu-genai==1.4.2",
-                ]
+                extras_require_map["fbgemm-gpu"] = ["fbgemm-gpu-genai==1.4.1"]
                 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"]
                 extras_require_map["vllm"] = ["vllm==0.11.0"]
-                if not install_xformers:
-                    _install_requires.pop(_install_requires.index(xformers_version))
             elif (major, minor) >= (2, 7):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:
-                    if install_xformers:
-                        _install_requires.append("xformers==0.0.30")
+                    _install_requires.append("xformers==0.0.30")
                     # vllm 0.9.x is incompatible with latest transformers
                     extras_require_map.pop("vllm")
                 else:
-                    if install_xformers:
-                        _install_requires.append("xformers==0.0.31")
+                    _install_requires.append("xformers==0.0.31")
                     extras_require_map["vllm"] = ["vllm==0.10.1"]
             elif (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
-                if install_xformers:
-                    _install_requires.append("xformers==0.0.29.post3")
+                _install_requires.append("xformers==0.0.29.post3")
                 # since we only support 2.6.0+cu126
                 _dependency_links.append("https://download.pytorch.org/whl/cu126")
                 extras_require_map.pop("vllm")
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
-                if install_xformers:
-                    if patch == 0:
-                        _install_requires.append("xformers==0.0.28.post2")
-                    else:
-                        _install_requires.append("xformers>=0.0.28.post3")
+                if patch == 0:
+                    _install_requires.append("xformers==0.0.28.post2")
+                else:
+                    _install_requires.append("xformers>=0.0.28.post3")
                 extras_require_map.pop("vllm")
             elif (major, minor) >= (2, 4):
                 extras_require_map.pop("vllm")
-                if install_xformers:
-                    if patch == 0:
-                        _install_requires.pop(_install_requires.index(xformers_version))
-                        _install_requires.append("xformers>=0.0.27")
-                    else:
-                        _install_requires.pop(_install_requires.index(xformers_version))
-                        _install_requires.append("xformers==0.0.28.post1")
+                if patch == 0:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers==0.0.28.post1")
             else:
                 raise ValueError("axolotl requires torch>=2.4")
 
@@ -134,11 +110,15 @@ def parse_requirements(extras_require_map):
 
 def get_package_version():
     with open(
-        Path(os.path.dirname(os.path.abspath(__file__))) / "VERSION",
+        Path(os.path.dirname(os.path.abspath(__file__)))
+        / "src"
+        / "axolotl"
+        / "__init__.py",
         "r",
         encoding="utf-8",
     ) as fin:
-        version_ = fin.read().strip()
+        version_match = re.search(r"^__version__\s*=\s*(.*)$", fin.read(), re.MULTILINE)
+    version_ = ast.literal_eval(version_match.group(1))
     return version_
 
 

src/axolotl/__init__.py

@@ -1,11 +1,7 @@
 """Axolotl - Train and fine-tune large language models"""
 
 import pkgutil
-from importlib.metadata import PackageNotFoundError, version
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-try:
-    __version__ = version("axolotl")
-except PackageNotFoundError:
-    __version__ = "unknown"
+__version__ = "0.13.0.dev"

src/axolotl/core/builders/causal.py

@@ -373,6 +373,11 @@ 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_dynamic_finetuning:
+            from axolotl.monkeypatch.loss.dft import dft_loss
+
+            trainer_kwargs["compute_loss_func"] = dft_loss
+
         trainer_cls = self._get_trainer_cls()
 
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(

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/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/monkeypatch/loss/dft.py

@@ -0,0 +1,98 @@
+"""Dynamic Fine-Tuning (DFT) loss implementation"""
+
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+
+
+def selective_log_softmax(logits, index):
+    """Memory-efficient log_softmax -> gather"""
+    if logits.dtype in [torch.float32, torch.float64]:
+        selected_logits = torch.gather(
+            logits, dim=-1, index=index.unsqueeze(-1)
+        ).squeeze(-1)
+        logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
+        per_token_logps = selected_logits - logsumexp_values
+    else:
+        per_token_logps = []
+        for row_logits, row_labels in zip(logits, index, strict=True):
+            row_logps = F.log_softmax(row_logits, dim=-1)
+            row_per_token_logps = row_logps.gather(
+                dim=-1, index=row_labels.unsqueeze(-1)
+            ).squeeze(-1)
+            per_token_logps.append(row_per_token_logps)
+        per_token_logps = torch.stack(per_token_logps)
+    return per_token_logps
+
+
+def get_dft_loss(ignore_index: int = -100):
+    """Creates DFT loss function"""
+
+    def for_causal_lm_dft_loss(
+        logits,
+        labels,
+        vocab_size: int = None,
+        num_items_in_batch: Optional[int] = None,
+        ignore_index: int = -100,
+        shift_labels: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """DFT loss: -exp(logprobs).detach() * logprobs"""
+        if shift_labels is None:
+            # Shift so that tokens < n predict n
+            labels = F.pad(labels, (0, 1), value=ignore_index)
+            shift_labels = labels[..., 1:].contiguous()
+
+        shift_labels = shift_labels.to(logits.device)
+
+        # Create loss mask
+        loss_mask = shift_labels != ignore_index
+        shift_labels_masked = shift_labels.clone()
+        shift_labels_masked[~loss_mask] = 0
+
+        # Compute log probabilities
+        logprobs = selective_log_softmax(logits, shift_labels_masked)
+
+        # DFT loss: -exp(logprobs).detach() * logprobs
+        per_token_loss = -logprobs.exp().detach() * logprobs
+
+        # Sum over valid tokens and normalize
+        if num_items_in_batch is None:
+            num_items_in_batch = loss_mask.sum()
+
+        loss = (per_token_loss * loss_mask).sum() / num_items_in_batch
+        return loss
+
+    return for_causal_lm_dft_loss
+
+
+def dft_loss(outputs, labels, num_items_in_batch=None):
+    """DFT loss compatible with Trainer.compute_loss_func signature.
+
+    This function is designed to be passed to Trainer's compute_loss_func parameter.
+    """
+    ignore_index = -100
+
+    # Shift labels for causal LM
+    labels = F.pad(labels, (0, 1), value=ignore_index)
+    shift_labels = labels[..., 1:].contiguous()
+    shift_labels = shift_labels.to(outputs.logits.device)
+
+    # Create loss mask
+    loss_mask = shift_labels != ignore_index
+    shift_labels_masked = shift_labels.clone()
+    shift_labels_masked[~loss_mask] = 0
+
+    # Compute log probabilities
+    logprobs = selective_log_softmax(outputs.logits, shift_labels_masked)
+
+    # DFT loss: -exp(logprobs).detach() * logprobs
+    per_token_loss = -logprobs.exp().detach() * logprobs
+
+    # Sum over valid tokens and normalize
+    if num_items_in_batch is None:
+        num_items_in_batch = loss_mask.sum()
+
+    loss = (per_token_loss * loss_mask).sum() / num_items_in_batch
+    return loss

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/schemas/config.py

@@ -676,6 +676,10 @@ class AxolotlInputConfig(
             "description": "Number of chunks to use for chunked cross entropy loss"
         },
     )
+    use_dynamic_finetuning: bool | None = Field(
+        default=None,
+        json_schema_extra={"description": "Enable Dynamic Fine-Tuning loss (DFT)"},
+    )
 
     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/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."""

tests/core/test_builders.py

@@ -311,6 +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/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)