add validation for DFT

.github/PULL_REQUEST_TEMPLATE.md

@@ -15,11 +15,6 @@
 <!--- Include details of your testing environment, tests ran to see how -->
 <!--- your change affects other areas of the code, etc. -->
 
-## AI Usage Disclaimer
-
-<!--- Was AI (e.g., ChatGPT, Claude, Copilot) used to generate or assist with this PR? -->
-<!--- Please indicate: No / Yes (specify which tool and to what extent) -->
-
 ## Screenshots (if appropriate)
 
 ## Types of changes

.github/workflows/base.yml

@@ -21,8 +21,6 @@ jobs:
     timeout-minutes: 480
     # this job needs to be run on self-hosted GPU runners...
     runs-on: ubuntu-latest-m
-    env:
-      HAS_DOCKERHUB_CREDS: ${{ secrets.DOCKERHUB_USERNAME != '' && secrets.DOCKERHUB_TOKEN != '' }}
     strategy:
       fail-fast: false
       matrix:
@@ -34,7 +32,6 @@ jobs:
             pytorch: 2.8.0
             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"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -42,7 +39,6 @@ jobs:
             pytorch: 2.9.0
             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: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -50,7 +46,6 @@ jobs:
             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: ""
@@ -58,7 +53,6 @@ jobs:
             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: ""
@@ -85,7 +79,7 @@ jobs:
             axolotlai/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
-        if: ${{ github.event_name != 'pull_request' && env.HAS_DOCKERHUB_CREDS == 'true' }}
+        if: ${{ github.event_name != 'pull_request' && secrets.DOCKERHUB_USERNAME != '' && secrets.DOCKERHUB_TOKEN != '' }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
@@ -96,7 +90,7 @@ jobs:
         with:
           context: .
           file: ./docker/${{ matrix.dockerfile }}
-          platforms: ${{ matrix.platforms }}
+          platforms: linux/amd64,linux/arm64
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}
@@ -111,8 +105,6 @@ jobs:
     if: ${{ github.repository_owner == 'axolotl-ai-cloud' && (github.event_name != 'pull_request' || !github.event.pull_request.draft) }}
     timeout-minutes: 480
     runs-on: ubuntu-latest-m
-    env:
-      HAS_DOCKERHUB_CREDS: ${{ secrets.DOCKERHUB_USERNAME != '' && secrets.DOCKERHUB_TOKEN != '' }}
     strategy:
       fail-fast: false
       matrix:
@@ -124,7 +116,6 @@ jobs:
             pytorch: 2.8.0
             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"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -132,7 +123,6 @@ jobs:
             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: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -140,7 +130,6 @@ jobs:
             pytorch: 2.9.0
             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: ""
@@ -148,7 +137,6 @@ jobs:
             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
@@ -160,7 +148,6 @@ jobs:
             axolotlai/axolotl-base-uv
       - name: Login to Docker Hub
         uses: docker/login-action@v2
-        if: ${{ github.event_name != 'pull_request' && env.HAS_DOCKERHUB_CREDS == 'true' }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
@@ -171,7 +158,6 @@ jobs:
         with:
           context: .
           file: ./docker/${{ matrix.dockerfile }}
-          platforms: ${{ matrix.platforms }}
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}

.github/workflows/main.yml

@@ -20,26 +20,22 @@ jobs:
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
-            platforms: "linux/amd64"
+            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.0
             axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-            is_latest: true
           - cuda: 130
             cuda_version: 13.0.0
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -65,7 +61,7 @@ jobs:
         uses: docker/build-push-action@v5
         with:
           context: .
-          platforms: ${{ matrix.platforms }}
+          platforms: linux/amd64,linux/arm64
           build-args: |
             BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
             CUDA=${{ matrix.cuda }}
@@ -92,26 +88,22 @@ jobs:
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
-            platforms: "linux/amd64"
+            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.0
             axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
-            is_latest: true
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 130
             cuda_version: 13.0.0
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -136,7 +128,7 @@ jobs:
         uses: docker/build-push-action@v5
         with:
           context: .
-          platforms: ${{ matrix.platforms }}
+          platforms: linux/amd64,linux/arm64
           build-args: |
             BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
@@ -157,11 +149,11 @@ jobs:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.9.1
+            pytorch: 2.8.0
             axolotl_extras:
-            is_latest: true
+            is_latest:
-          - cuda: 130
+          - cuda: 128
-            cuda_version: 13.0.0
+            cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:

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

@@ -47,8 +47,7 @@ jobs:
             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]

docker/Dockerfile

@@ -6,7 +6,6 @@ ARG AXOLOTL_EXTRAS=""
 ARG AXOLOTL_ARGS=""
 ARG CUDA="118"
 ARG PYTORCH_VERSION="2.1.2"
-ARG TARGETARCH
 
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
 
@@ -21,17 +20,13 @@ RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
 
 WORKDIR /workspace/axolotl
 
-# If AXOLOTL_EXTRAS is set, append it in brackets; don't install deepspeed with arm64
+# If AXOLOTL_EXTRAS is set, append it in brackets
-RUN if [ "$TARGETARCH" = "arm64" ]; then \
+RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi && \
-    if [ "$AXOLOTL_EXTRAS" != "" ]; then \
+    python scripts/unsloth_install.py | sh && \
-        pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
-    else \
-        pip install --no-build-isolation -e .[$BASE_EXTRAS] $AXOLOTL_ARGS; \
-    fi && \    python scripts/unsloth_install.py | sh && \
     python scripts/cutcrossentropy_install.py | sh && \
     pip install pytest && \
     pip cache purge

docker/Dockerfile-uv-base

@@ -2,7 +2,6 @@ ARG CUDA_VERSION="12.6.3"
 ARG CUDNN_VERSION=""
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4
-ARG TARGETARCH
 
 FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
 
@@ -32,35 +31,20 @@ ENV PATH="/workspace/axolotl-venv/bin:${PATH}"
 
 RUN uv pip install packaging setuptools wheel psutil \
     && uv pip install torch==${PYTORCH_VERSION} torchvision \
+    && uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
+    && uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
     && uv pip install awscli pydantic
 
-RUN if [ "$TARGETARCH" = "amd64" ]; then \
-        uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main"; \
-        uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"; \
-    fi
-
 RUN case "$PYTORCH_VERSION" in \
         2.9.[0-9]*) \
-            if [ "$TARGETARCH" = "amd64" ]; then \
+            if [ "$CUDA" = "128" ]; then \
-                if [ "$CUDA" = "128" ]; then \
+                wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+                uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+                rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+            elif [ "$CUDA" = "130" ]; then \
-                elif [ "$CUDA" = "130" ]; then \
+                wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
+                uv pip install --no-cache-dir flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
+                rm flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    rm flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-                fi \
-            elif [ "$TARGETARCH" = "arm64" ]; then \
-                if [ "$CUDA" = "128" ]; then \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.6.4/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
-                elif [ "$CUDA" = "130" ]; then \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.6.4/flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    rm flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
-                fi \
             fi \
             ;; \
     esac

examples/gemma3/gemma-3-1b-qlora.yml

@@ -1,7 +1,6 @@
 base_model: google/gemma-3-1b-it
 
 model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
@@ -30,7 +29,7 @@ output_dir: ./outputs/out
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
+lora_dropout: 0.05
 lora_target_linear: true
 
 sequence_len: 2048

examples/gemma3/gemma-3-270m-qlora.yml

@@ -1,7 +1,6 @@
 base_model: google/gemma-3-270m-it
 
 model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
@@ -30,7 +29,7 @@ output_dir: ./outputs/out
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
+lora_dropout: 0.05
 lora_target_linear: true
 
 sequence_len: 2048

examples/gemma3/gemma-3-4b-qlora.yml

@@ -2,7 +2,6 @@ base_model: google/gemma-3-4b-it
 
 # Need to set else transformers tries to load vision too
 model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
 
 load_in_4bit: true
 
@@ -33,8 +32,8 @@ sample_packing: true
 
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
+lora_dropout: 0.05
-lora_target_linear: true
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:
 wandb_entity:

examples/gemma3/gemma-3-4b-vision-qlora.yml

@@ -31,7 +31,7 @@ pad_to_sequence_len: false
 
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
+lora_dropout: 0.05
 lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:

requirements.txt

@@ -11,11 +11,11 @@ liger-kernel==0.6.4
 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==0.25.1
 hf_xet==1.2.0

setup.py

@@ -26,7 +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 = [
@@ -63,63 +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"] = [
+                extras_require_map["fbgemm-gpu"] = ["fbgemm-gpu-genai==1.4.1"]
-                    "fbgemm-gpu==1.4.0",
-                    "fbgemm-gpu-genai==1.4.2",
-                ]
                 extras_require_map["vllm"] = ["vllm==0.11.1"]
-                if not install_xformers:
-                    _install_requires.pop(_install_requires.index(xformers_version))
             elif (major, minor) >= (2, 8):
                 extras_require_map.pop("fbgemm-gpu")
                 extras_require_map["fbgemm-gpu"] = ["fbgemm-gpu-genai==1.3.0"]
                 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:
-                    if patch == 0:
+                    _install_requires.append("xformers==0.0.28.post2")
-                        _install_requires.append("xformers==0.0.28.post2")
+                else:
-                    else:
+                    _install_requires.append("xformers>=0.0.28.post3")
-                        _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:
-                    if patch == 0:
+                    _install_requires.pop(_install_requires.index(xformers_version))
-                        _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
-                        _install_requires.append("xformers>=0.0.27")
+                else:
-                    else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
-                        _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers==0.0.28.post1")
-                        _install_requires.append("xformers==0.0.28.post1")
             else:
                 raise ValueError("axolotl requires torch>=2.4")
 

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.13.1"
+__version__ = "0.13.0.dev"

src/axolotl/loaders/patch_manager.py

@@ -153,9 +153,12 @@ class PatchManager:
             from axolotl.monkeypatch.loss.chunked import patch_chunked_ce_loss_fn
 
             if self.cfg.chunked_cross_entropy_num_chunks:
-                patch_chunked_ce_loss_fn(self.cfg.chunked_cross_entropy_num_chunks)
+                patch_chunked_ce_loss_fn(
+                    self.cfg.chunked_cross_entropy_num_chunks,
+                    use_dft=self.cfg.use_dynamic_finetuning,
+                )
             else:
-                patch_chunked_ce_loss_fn()
+                patch_chunked_ce_loss_fn(use_dft=self.cfg.use_dynamic_finetuning)
 
     def _apply_fsdp_patches(self):
         """Apply patches for FSDP configurations."""

src/axolotl/loaders/utils.py

@@ -5,7 +5,6 @@ from typing import Type
 
 import addict
 import torch
-import transformers
 from transformers import AutoConfig, PretrainedConfig, PreTrainedModel
 
 from axolotl.utils.dict import DictDefault
@@ -154,9 +153,6 @@ def load_model_config(cfg: DictDefault) -> PretrainedConfig | addict.Dict:
     This function determines the appropriate model config source, loads it, applies any
     necessary overrides, and validates it for compatibility with the `axolotl` config.
 
-    If `cfg.cls_model_config` is set, a custom config class from transformers will be
-    used instead of `AutoConfig` (e.g., 'LlamaConfig', 'MistralConfig').
-
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
 
@@ -178,13 +174,8 @@ def load_model_config(cfg: DictDefault) -> PretrainedConfig | addict.Dict:
     if cfg.num_labels:
         # num_labels is used to initialize classifier models
         config_kwargs["num_labels"] = cfg.num_labels
-
-    config_cls = AutoConfig
-    if cfg.cls_model_config:
-        config_cls = getattr(transformers, cfg.cls_model_config)
-
     try:
-        model_config = config_cls.from_pretrained(
+        model_config = AutoConfig.from_pretrained(
             model_config_name,
             trust_remote_code=trust_remote_code,
             **config_kwargs,

src/axolotl/monkeypatch/loss/chunked.py

@@ -16,10 +16,16 @@ class CEWithChunkedOutputLoss(torch.nn.Module):
     For more details, please refer to: https://github.com/pytorch/torchtune/pull/1390
     """
 
-    def __init__(self, num_output_chunks: int = 8, ignore_index: int = -100):
+    def __init__(
+        self,
+        num_output_chunks: int = 8,
+        ignore_index: int = -100,
+        use_dft: bool = False,
+    ):
         super().__init__()
         self.num_output_chunks = num_output_chunks
         self.ignore_index = ignore_index
+        self.use_dft = use_dft
 
     def compute_cross_entropy(
         self,
@@ -30,10 +36,30 @@ class CEWithChunkedOutputLoss(torch.nn.Module):
         """
         Upcast logits to fp32 and compute cross entropy loss.
         """
-        return F.cross_entropy(
+        ce_loss = F.cross_entropy(
-            logits.float(), labels, ignore_index=self.ignore_index, reduction="sum"
+            logits.float(), labels, ignore_index=self.ignore_index, reduction="none"
         )
 
+        if self.use_dft:
+            # Compute probabilities and gather the ones corresponding to labels
+            with torch.no_grad():  # Stop gradient
+                probs = torch.softmax(logits.float(), dim=-1)
+                # Create mask for valid tokens (not ignore_index)
+                valid_mask = labels != self.ignore_index
+                # Gather probabilities for the correct tokens
+                label_probs = probs.gather(-1, labels.unsqueeze(-1)).squeeze(-1)
+                # Apply mask to only scale valid tokens
+                label_probs = label_probs * valid_mask
+                # Avoid multiplication by 0 for ignored tokens
+                label_probs = torch.where(
+                    valid_mask, label_probs, torch.ones_like(label_probs)
+                )
+
+            # Scale the loss by the probability (DFT)
+            ce_loss = ce_loss * label_probs
+
+        return ce_loss.sum()
+
     def forward(
         self, logits: List[torch.Tensor], labels: torch.Tensor, reduction="sum"
     ) -> torch.Tensor:
@@ -71,16 +97,20 @@ class CEWithChunkedOutputLoss(torch.nn.Module):
         return total_loss / total_elements
 
 
-def _build_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
+def _build_chunked_ce_loss_fn(
-    loss_fn_ce = CEWithChunkedOutputLoss(num_output_chunks, ignore_index)
+    num_output_chunks: int = 8, ignore_index: int = -100, use_dft: bool = False
+):
+    loss_fn_ce = CEWithChunkedOutputLoss(num_output_chunks, ignore_index, use_dft)
     loss_fn_ce.compute_cross_entropy = torch.compile(
         loss_fn_ce.compute_cross_entropy, backend="inductor"
     )
     return loss_fn_ce
 
 
-def get_causal_lm_loss(num_output_chunks: int = 8, ignore_index: int = -100):
+def get_causal_lm_loss(
-    loss_fn_ce = _build_chunked_ce_loss_fn(num_output_chunks, ignore_index)
+    num_output_chunks: int = 8, ignore_index: int = -100, use_dft: bool = False
+):
+    loss_fn_ce = _build_chunked_ce_loss_fn(num_output_chunks, ignore_index, use_dft)
 
     def chunked_fix_cross_entropy(
         source,
@@ -124,10 +154,14 @@ def get_causal_lm_loss(num_output_chunks: int = 8, ignore_index: int = -100):
     return for_causal_lm_chunked_loss
 
 
-def patch_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
+def patch_chunked_ce_loss_fn(
+    num_output_chunks: int = 8, ignore_index: int = -100, use_dft: bool = False
+):
     import transformers.loss.loss_utils
 
-    for_causal_lm_chunked_loss = get_causal_lm_loss(num_output_chunks, ignore_index)
+    for_causal_lm_chunked_loss = get_causal_lm_loss(
+        num_output_chunks, ignore_index, use_dft
+    )
     transformers.loss.loss_utils.ForCausalLMLoss = for_causal_lm_chunked_loss
     transformers.loss.loss_utils.LOSS_MAPPING["ForCausalLM"] = (
         for_causal_lm_chunked_loss

src/axolotl/utils/schemas/config.py

@@ -664,6 +664,13 @@ class AxolotlInputConfig(
         },
     )
 
+    use_dynamic_finetuning: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Whether to use dynamic fine-tuning for scaled SFT gradients."
+        },
+    )
+
     chunked_cross_entropy: bool | None = Field(
         default=None,
         json_schema_extra={

src/axolotl/utils/schemas/model.py

@@ -25,12 +25,7 @@ class ModelInputConfig(BaseModel):
             "description": "If the base_model repo on hf hub doesn't include configuration .json files, You can set that here, or leave this empty to default to base_model"
         },
     )
-    cls_model_config: str | None = Field(
+    cls_model_config: str | None = None
-        default=None,
-        json_schema_extra={
-            "description": "transformers config class (e.g., 'LlamaConfig', 'MistralConfig'). Defaults to AutoConfig."
-        },
-    )
     tokenizer_config: str | None = Field(
         default=None,
         json_schema_extra={

src/axolotl/utils/schemas/validation.py

@@ -434,6 +434,18 @@ class TrainingValidationMixin:
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_ao_optim_fsdp2_offload(cls, data):
+        if data.get("fsdp_config") and data.get("fsdp_config", {}).get(
+            "offload_params"
+        ):
+            if data.get("optimizer") in ["adamw_torch_8bit", "adamw_torch_4bit"]:
+                raise ValueError(
+                    "low bit ao optimizers is not supported with FSDP2 w/ offload_params."
+                )
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_use_reentrant_mismatch(cls, data):
@@ -557,6 +569,20 @@ class TrainingValidationMixin:
         return data
 
 
+class CELossValidationMixin:
+    """Validation methods related to CE loss configuration."""
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_dft_loss_fn(cls, data):
+        if data.get("use_dynamic_finetuning"):
+            if not data.get("chunked_cross_entropy"):
+                raise ValueError(
+                    "`use_dynamic_finetuning` requires `chunked_cross_entropy`"
+                )
+        return data
+
+
 class LoRAValidationMixin:
     """Validation methods related to LoRA/QLoRA configuration."""
 
@@ -1464,6 +1490,7 @@ class ValidationMixin(
     DatasetValidationMixin,
     AttentionValidationMixin,
     TrainingValidationMixin,
+    CELossValidationMixin,
     LoRAValidationMixin,
     RLValidationMixin,
     OptimizationValidationMixin,