remove monkeypatch

* fix syntax  for secrets in gha yaml

* setup env for uv too

* arm64 for base  uv too

* don't build causal-conv1d or mamba for arm64 and use arm64 wheels

* fix dockerfile syntax

* fix shell syntax

.github/PULL_REQUEST_TEMPLATE.md

@@ -15,6 +15,11 @@
 <!--- 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

@@ -21,6 +21,8 @@ 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:
@@ -32,6 +34,7 @@ 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: ""
@@ -39,6 +42,7 @@ 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: ""
@@ -46,6 +50,7 @@ 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: ""
@@ -53,6 +58,7 @@ 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: ""
@@ -79,7 +85,7 @@ jobs:
             axolotlai/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
-        if: ${{ github.event_name != 'pull_request' && secrets.DOCKERHUB_USERNAME != '' && secrets.DOCKERHUB_TOKEN != '' }}
+        if: ${{ github.event_name != 'pull_request' && env.HAS_DOCKERHUB_CREDS == 'true' }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
@@ -90,7 +96,7 @@ jobs:
         with:
           context: .
           file: ./docker/${{ matrix.dockerfile }}
-          platforms: linux/amd64,linux/arm64
+          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 }}
@@ -105,6 +111,8 @@ 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:
@@ -116,6 +124,7 @@ 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: ""
@@ -123,6 +132,7 @@ 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: ""
@@ -130,6 +140,7 @@ 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: ""
@@ -137,6 +148,7 @@ 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
@@ -148,6 +160,7 @@ 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: ${{ secrets.DOCKERHUB_TOKEN }}
@@ -158,6 +171,7 @@ jobs:
         with:
           context: .
           file: ./docker/${{ 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

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

docker/Dockerfile

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

@@ -2,6 +2,7 @@ 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
 
@@ -31,20 +32,35 @@ 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 [ "$CUDA" = "128" ]; then \
-                wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-                uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-                rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-            elif [ "$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; \
-                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; \
+            if [ "$TARGETARCH" = "amd64" ]; then \
+                if [ "$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; \
+                    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; \
+                elif [ "$CUDA" = "130" ]; then \
+                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
+                    uv pip install --no-cache-dir flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
+                    rm flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
+                fi \
+            elif [ "$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-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/gemma3/gemma-3-1b-qlora.yml

@@ -1,6 +1,7 @@
 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
@@ -29,7 +30,7 @@ output_dir: ./outputs/out
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0.05
+lora_dropout: 0
 lora_target_linear: true
 
 sequence_len: 2048

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

@@ -1,6 +1,7 @@
 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
@@ -29,7 +30,7 @@ output_dir: ./outputs/out
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0.05
+lora_dropout: 0
 lora_target_linear: true
 
 sequence_len: 2048

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

@@ -2,6 +2,7 @@ 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
 
@@ -32,8 +33,8 @@ sample_packing: true
 
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_dropout: 0
+lora_target_linear: true
 
 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: 0.05
+lora_dropout: 0
 lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:

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/loaders/patch_manager.py

@@ -153,12 +153,9 @@ 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,
-                    use_dft=self.cfg.use_dynamic_finetuning,
-                )
+                patch_chunked_ce_loss_fn(self.cfg.chunked_cross_entropy_num_chunks)
             else:
-                patch_chunked_ce_loss_fn(use_dft=self.cfg.use_dynamic_finetuning)
+                patch_chunked_ce_loss_fn()
 
     def _apply_fsdp_patches(self):
         """Apply patches for FSDP configurations."""

src/axolotl/loaders/utils.py

@@ -5,6 +5,7 @@ from typing import Type
 
 import addict
 import torch
+import transformers
 from transformers import AutoConfig, PretrainedConfig, PreTrainedModel
 
 from axolotl.utils.dict import DictDefault
@@ -153,6 +154,9 @@ 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.
 
@@ -174,8 +178,13 @@ 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 = AutoConfig.from_pretrained(
+        model_config = config_cls.from_pretrained(
             model_config_name,
             trust_remote_code=trust_remote_code,
             **config_kwargs,

src/axolotl/monkeypatch/loss/chunked.py

@@ -16,16 +16,10 @@ 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,
-        use_dft: bool = False,
-    ):
+    def __init__(self, num_output_chunks: int = 8, ignore_index: int = -100):
         super().__init__()
         self.num_output_chunks = num_output_chunks
         self.ignore_index = ignore_index
-        self.use_dft = use_dft
 
     def compute_cross_entropy(
         self,
@@ -36,30 +30,10 @@ class CEWithChunkedOutputLoss(torch.nn.Module):
         """
         Upcast logits to fp32 and compute cross entropy loss.
         """
-        ce_loss = F.cross_entropy(
-            logits.float(), labels, ignore_index=self.ignore_index, reduction="none"
+        return F.cross_entropy(
+            logits.float(), labels, ignore_index=self.ignore_index, reduction="sum"
         )
 
-        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:
@@ -97,20 +71,16 @@ 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, use_dft: bool = False
-):
-    loss_fn_ce = CEWithChunkedOutputLoss(num_output_chunks, ignore_index, use_dft)
+def _build_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
+    loss_fn_ce = CEWithChunkedOutputLoss(num_output_chunks, ignore_index)
     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, use_dft: bool = False
-):
-    loss_fn_ce = _build_chunked_ce_loss_fn(num_output_chunks, ignore_index, use_dft)
+def get_causal_lm_loss(num_output_chunks: int = 8, ignore_index: int = -100):
+    loss_fn_ce = _build_chunked_ce_loss_fn(num_output_chunks, ignore_index)
 
     def chunked_fix_cross_entropy(
         source,
@@ -154,14 +124,10 @@ def get_causal_lm_loss(
     return for_causal_lm_chunked_loss
 
 
-def patch_chunked_ce_loss_fn(
-    num_output_chunks: int = 8, ignore_index: int = -100, use_dft: bool = False
-):
+def patch_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
     import transformers.loss.loss_utils
 
-    for_causal_lm_chunked_loss = get_causal_lm_loss(
-        num_output_chunks, ignore_index, use_dft
-    )
+    for_causal_lm_chunked_loss = get_causal_lm_loss(num_output_chunks, ignore_index)
     transformers.loss.loss_utils.ForCausalLMLoss = for_causal_lm_chunked_loss
     transformers.loss.loss_utils.LOSS_MAPPING["ForCausalLM"] = (
         for_causal_lm_chunked_loss

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/schemas/config.py

@@ -664,13 +664,6 @@ 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={
@@ -683,6 +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/model.py

@@ -25,7 +25,12 @@ 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 = None
+    cls_model_config: str | None = Field(
+        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

@@ -434,18 +434,6 @@ 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):
@@ -569,20 +557,6 @@ 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."""
 
@@ -1490,7 +1464,6 @@ class ValidationMixin(
     DatasetValidationMixin,
     AttentionValidationMixin,
     TrainingValidationMixin,
-    CELossValidationMixin,
     LoRAValidationMixin,
     RLValidationMixin,
     OptimizationValidationMixin,