wire up squash_position_ids

* make multipack sampler patch explicit

* combining

.coderabbit.yaml

@@ -12,5 +12,6 @@ reviews:
   auto_review:
     enabled: true
     drafts: false
+    auto_incremental_review: true
 chat:
   auto_reply: true

.github/CONTRIBUTING.md

@@ -57,6 +57,13 @@ We welcome ideas for improvements and new features. To suggest an enhancement, o
 5. Push your branch to your fork on GitHub.
 6. Open a new pull request against the `main` branch of the axolotl repository. Include a clear and concise description of your changes, referencing any related issues.
 
+#### Skipping CI Checks
+
+You can skip certain CI checks by including specific keywords in your commit messages:
+
+- `[skip ci]` or `skip ci` - Skips all CI checks for that commit
+- `[skip-e2e]` or `skip-e2e` - Skips only end-to-end tests while running other CI checks. You may also include this in the title of your PR to disable end-to-end tests for the entire PR.
+
 ## Style Guidelines
 
 ### Code Style

.github/workflows/tests.yml

@@ -188,13 +188,44 @@ jobs:
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
+  gate-skip-e2e:
+    needs: [pre-commit, pytest, pytest-sdist]
+    runs-on: ubuntu-latest
+    outputs:
+      skip: ${{ steps.compute.outputs.skip }}
+    steps:
+      - uses: actions/github-script@v7
+        id: compute
+        with:
+          script: |
+            const token = /\[skip-e2e\]/i;
+            let msg = '';
+            if (context.eventName === 'push') {
+              msg = context.payload.head_commit?.message || '';
+            } else if (context.eventName === 'pull_request') {
+              const { owner, repo } = context.repo;
+              const prNumber = context.payload.pull_request.number;
+              const commits = await github.paginate(
+                github.rest.pulls.listCommits,
+                { owner, repo, pull_number: prNumber, per_page: 100 }
+              );
+              msg = commits.at(-1)?.commit?.message || '';
+            }
+            const title = context.payload.pull_request?.title || '';
+            const body  = context.payload.pull_request?.body  || '';
+            const skip = token.test(msg) || token.test(title) || token.test(body);
+            core.setOutput('skip', String(skip));
+
   docker-e2e-tests-1st:
     # Run this job first as a gate for running the remainder of the test matrix
-    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' && !github.event.pull_request.draft }}
+    if: >
+      github.repository_owner == 'axolotl-ai-cloud' &&
+      (github.event_name != 'pull_request' || !github.event.pull_request.draft) &&
+      needs.gate-skip-e2e.outputs.skip != 'true'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
-    needs: [pre-commit, pytest, pytest-sdist]
+    needs: [pre-commit, pytest, pytest-sdist, gate-skip-e2e]
 
     strategy:
       fail-fast: false
@@ -240,13 +271,16 @@ jobs:
           modal run cicd.e2e_tests
 
   docker-e2e-tests:
-    if: ${{ github.repository_owner == 'axolotl-ai-cloud' && !github.event.pull_request.draft }}
+    if: >
+      github.repository_owner == 'axolotl-ai-cloud' &&
+      (github.event_name != 'pull_request' || !github.event.pull_request.draft) &&
+      needs.gate-skip-e2e.outputs.skip != 'true'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     # Only run the remainder of the matrix if the first e2e check passed;
     # this is to save on wasted compute costs for known failures that get caught in the first run
-    needs: [pre-commit, pytest, docker-e2e-tests-1st]
+    needs: [pre-commit, pytest, gate-skip-e2e, docker-e2e-tests-1st]
 
     strategy:
       fail-fast: false

TODO.md

@@ -1,10 +0,0 @@
-# todo list
-
-- [] Validation of parameters for combinations that won't work
-
-
-
-## things that are known not to work
-
-- FSDP offload and gradient_checkpointing - https://github.com/pytorch/pytorch/issues/82203
-- adamw_bnb_8bit doesn't play well with FSDP offload

docker/Dockerfile-base

@@ -37,7 +37,7 @@ WORKDIR /workspace
 
 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==${PYTORCH_VERSION}+cu${CUDA} torchvision --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
-    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
+    CAUSAL_CONV1D_FORCE_CXX11_ABI=TRUE CAUSAL_CONV1D_FORCE_BUILD=TRUE python3 -m pip install --no-cache-dir causal_conv1d==1.5.2 && \
     python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" && \
     python3 -m pip cache purge
 

docs/multimodal.qmd

@@ -13,10 +13,13 @@ format:
 - [Pixtral](#sec-pixtral)
 - [Llava-1.5](#sec-llava-15)
 - [Mistral-Small-3.1](#sec-mistral-small-31)
+- [Voxtral](#sec-voxtral)
 - [Gemma-3](#sec-gemma-3)
 - [Gemma-3n](#sec-gemma-3n)
 - [Qwen2-VL](#sec-qwen2-vl)
 - [Qwen2.5-VL](#sec-qwen25-vl)
+- [SmolVLM2](#sec-smolvlm2)
+- [LFM2-VL](#sec-lfm2-vl)
 
 ## Usage
 
@@ -31,7 +34,7 @@ skip_prepare_dataset: true
 remove_unused_columns: false  # leave columns in place as they are needed to handle image embeddings during training
 sample_packing: false  # not yet supported with multimodal
 
-chat_template:  # see in next section
+chat_template:  # see in next section if specified
 
 # example dataset
 datasets:
@@ -97,6 +100,16 @@ base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
 chat_template: mistral_v7_tekken
 ```
 
+### Voxtral {#sec-voxtral}
+
+::: {.callout-tip}
+Please make sure to install audio lib via `pip3 install librosa==0.11.0 'mistral_common[audio]==1.8.3'`
+:::
+
+```yaml
+base_model: mistralai/Voxtral-Mini-3B-2507
+```
+
 ### Gemma-3 {#sec-gemma-3}
 
 ::: {.callout-tip}
@@ -143,6 +156,26 @@ base_model: Qwen/Qwen2.5-VL-7B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
+### SmolVLM2 {#sec-smolvlm2}
+
+::: {.callout-tip}
+Please make sure to install `num2words` via `pip3 install num2words==0.5.14`
+:::
+
+```yaml
+base_model: HuggingFaceTB/SmolVLM2-500M-Video-Instruct
+```
+
+### LFM2-VL {#sec-lfm2-vl}
+
+::: {.callout-warning}
+Please uninstall `causal-conv1d` via `pip3 uninstall -y causal-conv1d`
+:::
+
+```yaml
+base_model: LiquidAI/LFM2-VL-450M
+```
+
 ## Dataset Format
 
 For multi-modal datasets, we adopt an extended `chat_template` format similar to OpenAI's Message format.
@@ -181,6 +214,20 @@ You may need to install `librosa` via `pip3 install librosa==0.11.0`.
 
 :::
 
+### Video
+
+::: {.callout-warning}
+
+This is not well tested at the moment. We welcome contributors!
+
+:::
+
+For video loading, you can use the following keys within `content` alongside `"type": "video"`:
+
+- `"path": "/path/to/video.mp4"`
+- `"url": "https://example.com/video.mp4"`
+- `"video": np.ndarray | list[PIL.Image.Image] | torch.Tensor` (or list of the aforementioned)
+
 ### Example
 
 Here is an example of a multi-modal dataset:

examples/LiquidAI/README.md

@@ -0,0 +1,58 @@
+# Finetune Liquid Foundation Models 2 (LFM2) with Axolotl
+
+[Liquid Foundation Models 2 (LFM2)](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38) are a family of small, open-weight models from [Liquid AI](https://www.liquid.ai/) focused on quality, speed, and memory efficiency. Liquid AI released text-only [LFM2](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38) and text+vision [LFM2-VL](https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa) models.
+
+LFM2 features a new hybrid Liquid architecture with multiplicative gates, short-range convolutions, and grouped query attention, enabling fast training and inference.
+
+This guide shows how to fine-tune both the LFM2 and LFM2-VL models with Axolotl.
+
+## Getting Started
+
+1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+    Here is an example of how to install from pip:
+    ```bash
+    # Ensure you have a compatible version of Pytorch installed
+    pip3 install packaging setuptools wheel ninja
+    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+    ```
+
+2.  Run one of the finetuning examples below.
+
+    **LFM2**
+    ```bash
+    # FFT SFT (1x48GB @ 25GiB)
+    axolotl train examples/LiquidAI/lfm2-350m-fft.yaml
+    ```
+
+    **LFM2-VL**
+    ```bash
+    # LoRA SFT (1x48GB @ 2.7GiB)
+    axolotl train examples/LiquidAI/lfm2-vl-lora.yaml
+    ```
+
+### TIPS
+
+- **Installation Error**: If you encounter `ImportError: ... undefined symbol ...` or `ModuleNotFoundError: No module named 'causal_conv1d_cuda'`, the `causal-conv1d` package may have been installed incorrectly. Try uninstalling it:
+  ```bash
+  pip uninstall -y causal-conv1d
+  ```
+
+- **Dataset Loading**: Read more on how to load your own dataset in our [documentation](https://docs.axolotl.ai/docs/dataset_loading.html).
+- **Dataset Formats**:
+  - For LFM2 models, the dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+  - For LFM2-VL models, Axolotl follows the multi-content Messages format. See our [Multimodal docs](https://docs.axolotl.ai/docs/multimodal.html#dataset-format) for details.
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+
+## Related Resources
+
+- [LFM2 Blog](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models)
+- [LFM2-VL Blog](https://www.liquid.ai/blog/lfm2-vl-efficient-vision-language-models)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/LiquidAI/lfm2-350m-fft.yaml

@@ -2,7 +2,6 @@ base_model: LiquidAI/LFM2-350M
 
 chunked_cross_entropy: true
 
-chat_template: tokenizer_default
 eot_tokens:
   - "<|im_end|>"
 datasets:

examples/LiquidAI/lfm2-vl-lora.yaml

@@ -0,0 +1,58 @@
+base_model: LiquidAI/LFM2-VL-450M
+trust_remote_code: true
+model_type: AutoModelForImageTextToText
+processor_type: AutoProcessor
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/gpt-oss/README.md

@@ -33,13 +33,64 @@ Note: Memory usage taken from `device_mem_reserved(gib)` from logs.
 
 ### Training 120B
 
-On 8xH100s
+On 8xH100s, make sure you have ~3TB of free disk space. With each checkpoint clocking in at ~720GB, along with the base
+model, and final model output, you may need at least 3TB of free disk space to keep at least 2 checkpoints.
 
 ```bash
 # FFT SFT with offloading (8x80GB @ ~49GiB/GPU)
 axolotl train examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml
 ```
 
+To simplify fine-tuning across 2 nodes × 8x H100 (80GB) GPUs, we've partnered with [Baseten](https://baseten.co) to showcase multi-node
+training of the 120B model using Baseten Truss. You can read more about this recipe on
+[Baseten's blog](https://www.baseten.co/blog/how-to-fine-tune-gpt-oss-120b-with-baseten-and-axolotl/). The recipe can
+be found on their
+[GitHub](https://github.com/basetenlabs/ml-cookbook/tree/main/examples/oss-gpt-120b-axolotl/training).
+
+ERRATA: Transformers saves the model Architecture prefixed with `FSDP` which needs to be manually renamed in `config.json`.
+See https://github.com/huggingface/transformers/pull/40207 for the status of this issue.
+
+```bash
+sed -i 's/FSDPGptOssForCausalLM/GptOssForCausalLM/g' ./outputs/gpt-oss-out/config.json
+```
+
+When using SHARDED_STATE_DICT with FSDP, the final checkpoint should automatically merge the sharded weights to your
+configured `output_dir`. However, if that step fails due to a disk space error, you can take an additional step to
+merge the sharded weights.  This step will automatically determine the last checkpoint directory and merge the sharded
+weights to `{output_dir}/merged`.
+
+```bash
+axolotl merge-sharded-fsdp-weights examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml
+mv ./outputs/gpt-oss-out/merged/* ./outputs/gpt-oss-out/
+```
+
+
+### Inferencing your fine-tuned model
+
+#### vLLM
+
+GPT-OSS support in vLLM does not exist in a stable release yet. See https://x.com/MaziyarPanahi/status/1955741905515323425
+for more information about using a special vllm-openai docker image for inferencing with vLLM.
+
+Optionally, vLLM can be installed from nightly:
+
+```bash
+pip install --no-build-isolation --pre -U vllm --extra-index-url https://wheels.vllm.ai/nightly
+```
+and the vLLM server can be started with the following command (modify `--tensor-parallel-size 8` to match your environment):
+```bash
+vllm serve ./outputs/gpt-oss-out/ --served-model-name axolotl/gpt-oss-20b --host 0.0.0.0 --port 8888  --tensor-parallel-size 8
+```
+
+#### SGLang
+
+SGLang has 0-day support in main, see https://github.com/sgl-project/sglang/issues/8833 for infomation on installing
+SGLang from source. Once you've installed SGLang, run the following command to launch a SGLang server:
+
+```bash
+python3 -m sglang.launch_server --model ./outputs/gpt-oss-out/ --served-model-name axolotl/gpt-oss-120b --host 0.0.0.0 --port 8888 --tp 8
+```
+
 ### Tool use
 
 GPT-OSS has a comprehensive tool understanding. Axolotl supports tool calling datasets for Supervised Fine-tuning.

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

@@ -20,6 +20,7 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
+save_total_limit: 2  # the 120B model can use up to 720GB of disk space per checkpoint, so let's only keep the last 2
 
 sequence_len: 4096
 sample_packing: true
@@ -43,7 +44,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

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

@@ -40,7 +40,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

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

@@ -15,7 +15,7 @@ datasets:
     field_thinking: thinking
     template_thinking_key: thinking
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path: ./outputs/last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
 
@@ -41,7 +41,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

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

@@ -15,7 +15,7 @@ datasets:
     field_thinking: thinking
     template_thinking_key: thinking
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path: ./outputs/last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
 
@@ -40,7 +40,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

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

@@ -53,7 +53,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/lfm2/README.md

@@ -1,7 +0,0 @@
-# Liquid Foundation Models 2
-
-LFM2 support in transformers exists in the main branch, but is not yet included in the transformers release.
-
-```bash
-pip install --upgrade --no-deps --force-reinstall git+https://github.com/huggingface/transformers.git
-```

examples/smolvlm2/README.md

@@ -0,0 +1,49 @@
+# Finetune SmolVLM2 with Axolotl
+
+[SmolVLM2](https://huggingface.co/collections/HuggingFaceTB/smolvlm2-smallest-video-lm-ever-67ab6b5e84bf8aaa60cb17c7) are a family of lightweight, open-source multimodal models from HuggingFace designed to analyze and understand video, image, and text content.
+
+These models are built for efficiency, making them well-suited for on-device applications where computational resources are limited. Models are available in multiple sizes, including 2.2B, 500M, and 256M.
+
+This guide shows how to fine-tune SmolVLM2 models with Axolotl.
+
+## Getting Started
+
+1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+    Here is an example of how to install from pip:
+    ```bash
+    # Ensure you have a compatible version of Pytorch installed
+    pip3 install packaging setuptools wheel ninja
+    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+    ```
+
+2. Install an extra dependency:
+
+    ```bash
+    pip3 install num2words==0.5.14
+    ```
+
+3.  Run the finetuning example:
+
+    ```bash
+    # LoRA SFT (1x48GB @ 6.8GiB)
+    axolotl train examples/smolvlm2/smolvlm2-2B-lora.yaml
+    ```
+
+## TIPS
+
+- **Dataset Format**: For video finetuning, your dataset must be compatible with the multi-content Messages format. For more details, see our documentation on [Multimodal Formats](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
+- **Dataset Loading**: Read more on how to prepare and load your own datasets in our [documentation](https://docs.axolotl.ai/docs/dataset_loading.html).
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+
+## Related Resources
+
+- [SmolVLM2 Blog](https://huggingface.co/blog/smolvlm2)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/smolvlm2/smolvlm2-2B-lora.yaml

@@ -0,0 +1,56 @@
+base_model: HuggingFaceTB/SmolVLM2-2.2B-Instruct
+trust_remote_code: true
+processor_type: AutoProcessor
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'model.text_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

requirements.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.46.1
+bitsandbytes==0.47.0
 # triton 3.4.0 is not compatible with CCE
 triton>=3.0.0,<3.4.0
 mamba-ssm==1.2.0.post1
@@ -13,8 +13,8 @@ liger-kernel==0.6.1
 packaging==23.2
 
 huggingface_hub>=0.33.0
-peft==0.17.0
+peft>=0.17.0
-transformers==4.55.0
+transformers==4.55.3
 tokenizers>=0.21.1
 accelerate==1.10.0
 datasets==4.0.0

setup.py

@@ -118,9 +118,9 @@ def get_package_version():
 
 
 extras_require = {
-    "flash-attn": ["flash-attn==2.8.2"],
+    "flash-attn": ["flash-attn==2.8.3"],
     "ring-flash-attn": [
-        "flash-attn==2.8.2",
+        "flash-attn==2.8.3",
         "ring-flash-attn>=0.1.7",
         "yunchang==0.6.0",
     ],

src/axolotl/cli/args.py

@@ -40,6 +40,12 @@ class VllmServeCliArgs:
         default=None,
         metadata={"help": "Number of tensor parallel workers to use."},
     )
+    data_parallel_size: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "Number of data parallel workers to use for vLLM serving. This controls how many model replicas are used for parallel inference."
+        },
+    )
     host: Optional[str] = field(
         default=None,  # nosec B104
         metadata={"help": "Host address to run the server on."},

src/axolotl/cli/cloud/modal_.py

@@ -82,7 +82,7 @@ class ModalCloud(Cloud):
         return res
 
     def get_image(self):
-        docker_tag = "main-py3.11-cu124-2.6.0"
+        docker_tag = "main-py3.11-cu126-2.7.1"
         if self.config.docker_tag:
             docker_tag = self.config.docker_tag
         docker_image = f"axolotlai/axolotl:{docker_tag}"
@@ -200,7 +200,7 @@ class ModalCloud(Cloud):
         if family in ["a10", "a10g"]:
             return modal.gpu.A10G(count=count)
         if family == "h100":
-            return modal.gpu.H100(count=count)
+            return f"H100:{count}"
         if family == "t4":
             return modal.gpu.T4(count=count)
         if family == "l4":

src/axolotl/cli/inference.py

@@ -64,7 +64,7 @@ def do_inference(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template)
+        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
     elif cfg.datasets[0].type == "chat_template":
         chat_template_str = get_chat_template_from_config(
             cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -10,6 +10,7 @@ import fire
 import torch
 import torch.distributed.checkpoint as dist_cp
 import torch.distributed.checkpoint.format_utils as dist_cp_format_utils
+from accelerate import PartialState
 from accelerate.utils import (
     SAFE_WEIGHTS_INDEX_NAME,
     SAFE_WEIGHTS_NAME,
@@ -23,6 +24,7 @@ from torch.distributed.checkpoint.format_utils import _EmptyStateDictLoadPlanner
 
 from axolotl.cli.config import load_cfg
 from axolotl.utils.logging import get_logger
+from axolotl.utils.train import determine_last_checkpoint
 
 LOG = get_logger(__name__)
 
@@ -143,7 +145,6 @@ def merge_fsdp_weights(
         ValueError: If torch version < 2.3.0, or if `checkpoint_dir` does not exist.
     """
     checkpoint_dir_ = Path(checkpoint_dir)
-    from accelerate.state import PartialState
 
     if not is_torch_version(">=", "2.3.0"):
         raise ValueError("`merge_fsdp_weights` requires PyTorch >= 2.3.0`")
@@ -180,7 +181,6 @@ def merge_fsdp_weights(
         if remove_checkpoint_dir:
             LOG.info(f"Removing old checkpoint directory {checkpoint_dir_}")
             shutil.rmtree(checkpoint_dir_)
-    state.wait_for_everyone()
 
 
 def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
@@ -195,11 +195,32 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     parsed_cfg = load_cfg(config, **kwargs)
 
     fsdp_dir = Path(parsed_cfg.output_dir) / "pytorch_model_fsdp_0"
+    if not fsdp_dir.exists():
+        checkpoint_dir = determine_last_checkpoint(parsed_cfg, update=False)
+        if checkpoint_dir:
+            fsdp_dir = Path(checkpoint_dir) / "pytorch_model_fsdp_0"
+        if not fsdp_dir.exists():
+            raise ValueError(
+                f"Could not find FSDP checkpoint `pytorch_model_fsdp_0` in {checkpoint_dir}"
+            )
+
+    output_path = str(Path(parsed_cfg.output_dir) / "merged")
     merge_fsdp_weights(
         checkpoint_dir=str(fsdp_dir),
-        output_path=str(Path(parsed_cfg.output_dir) / "merged"),
+        output_path=output_path,
         safe_serialization=True,
     )
+    state = PartialState()
+    state.wait_for_everyone()
+    LOG.info(
+        f"FSDP SHARDED_STATE_DICT weights successfully merged to: {output_path}",
+        main_process_only=True,
+    )
+    LOG.info(
+        "Merged weights are only the safetensors and doesn't include the model configuration "
+        f"or tokenizer which may be found in {parsed_cfg.output_dir}.",
+        main_process_only=True,
+    )
 
 
 if __name__ == "__main__":

src/axolotl/cli/preprocess.py

@@ -97,7 +97,8 @@ def do_cli(
     """
     # pylint: disable=duplicate-code
     os.environ["AXOLOTL_IS_PREPROCESS"] = "1"
-    parsed_cfg = load_cfg(config, **kwargs)
+    is_preprocess = kwargs.pop("is_preprocess", True)
+    parsed_cfg = load_cfg(config, is_preprocess=is_preprocess, **kwargs)
     parsed_cfg.is_preprocess = True
     parser = transformers.HfArgumentParser(PreprocessCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/utils/sweeps.py

@@ -3,11 +3,12 @@
 import random
 from copy import deepcopy
 from itertools import product
+from typing import Any
 
 
 def generate_sweep_configs(
     base_config: dict[str, list], sweeps_config: dict[str, list]
-) -> list[dict[str, list]]:
+) -> list[dict[str, Any]]:
     """
     Recursively generates all possible configurations by applying sweeps to the base config.
 

src/axolotl/cli/utils/train.py

@@ -4,6 +4,7 @@ import os
 import subprocess  # nosec
 import sys
 import tempfile
+from pathlib import Path
 from typing import Any, Iterator, Literal
 
 import yaml
@@ -67,14 +68,12 @@ def build_command(base_cmd: list[str], options: dict[str, Any]) -> list[str]:
 
 def generate_config_files(config: str, sweep: str | None) -> Iterator[tuple[str, bool]]:
     """
-    Generate list of configuration files to process.
+    Generate list of configuration files to process. Yields a tuple of the configuration file name and a boolean indicating
+    whether this is a group of configurations (i.e., a sweep).
 
     Args:
         config: Base configuration file
         sweep: Sweep configuration file
-
-    Yields:
-        Tuple of configuration file name and whether this is a group of configurations
     """
 
     if not sweep:
@@ -90,7 +89,12 @@ def generate_config_files(config: str, sweep: str | None) -> Iterator[tuple[str,
     # Generate all possible configurations
     permutations = generate_sweep_configs(base_config, sweep_config)
     is_group = len(permutations) > 1
-    for permutation in permutations:
+    base_output_dir = base_config.get("output_dir", "./model-out")
+    for idx, permutation in enumerate(permutations, start=1):
+        permutation_dir = Path(permutation.get("output_dir", base_output_dir))
+        permutation_id = f"sweep{idx:04d}"
+        permutation["output_dir"] = str(permutation_dir / permutation_id)
+
         # pylint: disable=consider-using-with
         temp_file = tempfile.NamedTemporaryFile(
             mode="w",

src/axolotl/common/datasets.py

@@ -6,7 +6,6 @@ from dataclasses import dataclass
 
 from datasets import Dataset
 
-import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 from axolotl.cli.args import PreprocessCliArgs, TrainerCliArgs
 from axolotl.loaders import load_processor, load_tokenizer
 from axolotl.utils.data import prepare_datasets, prepare_preference_datasets

src/axolotl/core/builders/causal.py

@@ -476,6 +476,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 )
             ):
                 collator = V2BatchSamplerDataCollatorForSeq2Seq
+                if self.cfg.squash_position_ids:
+                    kwargs["squash_position_ids"] = True
             else:
                 collator = BatchSamplerDataCollatorForSeq2Seq
         else:

src/axolotl/core/trainers/__init__.py

@@ -5,7 +5,6 @@
 
 from .base import AxolotlTrainer
 from .dpo.trainer import AxolotlDPOTrainer
-from .grpo.trainer import AxolotlGRPOSequenceParallelTrainer, AxolotlGRPOTrainer
 from .mamba import AxolotlMambaTrainer
 from .trl import (
     AxolotlCPOTrainer,

src/axolotl/loaders/constants.py

@@ -1,26 +1,13 @@
 """Shared constants for axolotl.loaders module"""
 
-from transformers import (
+from transformers import AutoModelForImageTextToText
-    Gemma3ForConditionalGeneration,
+from transformers.models.auto.modeling_auto import (
-    Gemma3nForConditionalGeneration,
+    MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES,
-    Llama4ForConditionalGeneration,
-    LlavaForConditionalGeneration,
-    Mistral3ForConditionalGeneration,
-    MllamaForConditionalGeneration,
-    Qwen2_5_VLForConditionalGeneration,
-    Qwen2VLForConditionalGeneration,
 )
 
-MULTIMODAL_AUTO_MODEL_MAPPING = {
+MULTIMODAL_AUTO_MODEL_MAPPING = dict(MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES)
-    "mllama": MllamaForConditionalGeneration,
+
-    "llama4": Llama4ForConditionalGeneration,
+MULTIMODAL_AUTO_MODEL_MAPPING["lfm2-vl"] = AutoModelForImageTextToText
-    "llava": LlavaForConditionalGeneration,
-    "qwen2_vl": Qwen2VLForConditionalGeneration,
-    "qwen2_5_vl": Qwen2_5_VLForConditionalGeneration,
-    "mistral3": Mistral3ForConditionalGeneration,
-    "gemma3": Gemma3ForConditionalGeneration,
-    "gemma3n": Gemma3nForConditionalGeneration,
-}
 
 try:
     from transformers import VoxtralForConditionalGeneration

src/axolotl/loaders/model.py

@@ -25,6 +25,7 @@ from peft import (
 from torch.distributed import DeviceMesh
 from transformers import (
     AutoModelForCausalLM,
+    AutoModelForImageTextToText,
     AutoModelForVision2Seq,
     AwqConfig,
     BitsAndBytesConfig,
@@ -212,6 +213,7 @@ class ModelLoader:
             self.model_kwargs["use_kernels"] = self.cfg.use_kernels
         self._set_quantization_config()
         self._set_attention_config()
+        self._check_model_requirements()
 
     def _apply_post_model_load_setup(self):
         """Configure the model after it has been loaded."""
@@ -432,6 +434,8 @@ class ModelLoader:
             self.auto_model_loader = MULTIMODAL_AUTO_MODEL_MAPPING.get(
                 self.model_config.model_type, AutoModelForVision2Seq
             )
+            if isinstance(self.auto_model_loader, str):
+                self.auto_model_loader = AutoModelForImageTextToText
 
     def _set_device_map_config(self):
         """Setup `device_map` according to config"""
@@ -628,6 +632,16 @@ class ModelLoader:
         if self.cfg.low_cpu_mem_usage:
             self.model_kwargs["low_cpu_mem_usage"] = True
 
+    def _check_model_requirements(self):
+        if self.cfg.model_config_type in ["lfm2-vl", "lfm2"]:
+            from transformers.utils.import_utils import is_causal_conv1d_available
+
+            if is_causal_conv1d_available():
+                raise ImportError(
+                    "The 'causal-conv1d' package is installed but causes compatibility issues with LFM2 models. "
+                    "Please uninstall it by running: `pip uninstall -y causal-conv1d`"
+                )
+
     def _configure_zero3_memory_efficient_loading(
         self,
     ) -> HfTrainerDeepSpeedConfig | None:

src/axolotl/loaders/patch_manager.py

@@ -277,6 +277,14 @@ class PatchManager:
                 has_remote_code=has_remote_code,
             )
 
+        if self.cfg.sample_packing:
+            from axolotl.monkeypatch.data.batch_dataset_fetcher import (
+                apply_multipack_dataloader_patch,
+            )
+
+            LOG.info("Applying multipack dataloader patch for sample packing...")
+            apply_multipack_dataloader_patch()
+
     def _apply_fsdp2_bnb_patches(self):
         """Apply FSDP2 BNB patches."""
         if (
@@ -285,12 +293,10 @@ class PatchManager:
             and self.cfg.adapter == "qlora"
         ):
             from axolotl.monkeypatch.fsdp2_qlora import (
-                apply_bnb_torch_function_patch,
                 apply_init_sharded_param_patch,
                 apply_init_unsharded_param_patch,
             )
 
-            apply_bnb_torch_function_patch()
             apply_init_sharded_param_patch()
             apply_init_unsharded_param_patch()
 

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -187,7 +187,7 @@ def _process_lora_module_for_fsdp(module, fsdp2_kwargs):
 
     # Linear4Bit will keep it's bias term in fp32. If the weight dtype is in bf16 we are not able to
     # wrap this. Therefore we must ensure the bias has the same dtype as the weight
-    if module.base_layer.bias is not None:
+    if hasattr(module.base_layer, "bias") and module.base_layer.bias is not None:
         if module.base_layer.weight.dtype != module.base_layer.bias.dtype:
             log_bias_dtype_mismatch = True
             module.base_layer.bias.data = module.base_layer.bias.data.to(

src/axolotl/monkeypatch/data/batch_dataset_fetcher.py

@@ -1,4 +1,4 @@
-"""monkey patches for the dataset fetcher to handle batches of packed indexes"""
+"""Monkey patches for the dataset fetcher to handle batches of packed indexes."""
 
 # pylint: disable=protected-access
 
@@ -6,10 +6,20 @@ import torch
 from torch.utils.data._utils.fetch import _BaseDatasetFetcher
 from torch.utils.data._utils.worker import _worker_loop
 
+_ORIGINAL_MAP_DATASET_FETCHER = None
+_ORIGINAL_WORKER_LOOP = None
+_IS_PATCHED = False
+
 
 class _MapDatasetFetcher(_BaseDatasetFetcher):
+    """
+    Custom dataset fetcher that handles nested batch structures from
+    MultipackBatchSampler.
+    """
+
     def fetch(self, possibly_batched_index):
         if isinstance(possibly_batched_index[0], list):
+            # Handle nested structure from MultipackBatchSampler
             data = [None for i in possibly_batched_index]
             for i, possibly_batched_index_ in enumerate(possibly_batched_index):
                 if self.auto_collation:
@@ -23,6 +33,7 @@ class _MapDatasetFetcher(_BaseDatasetFetcher):
                 else:
                     data[i] = self.dataset[possibly_batched_index_]
         else:
+            # Standard batch handling
             if self.auto_collation:
                 if hasattr(self.dataset, "__getitems__") and self.dataset.__getitems__:
                     data = self.dataset.__getitems__(possibly_batched_index)
@@ -34,14 +45,54 @@ class _MapDatasetFetcher(_BaseDatasetFetcher):
 
 
 def patch_fetchers():
+    """Apply patches to PyTorch's DataLoader components."""
     torch.utils.data._utils.fetch._MapDatasetFetcher = _MapDatasetFetcher
     torch.utils.data.dataloader._utils.fetch._MapDatasetFetcher = _MapDatasetFetcher
 
 
 def patched_worker_loop(*args, **kwargs):
+    """Worker loop that ensures patches are applied in worker processes."""
     patch_fetchers()
     return _worker_loop(*args, **kwargs)
 
 
-torch.utils.data._utils.worker._worker_loop = patched_worker_loop
+def apply_multipack_dataloader_patch():
-patch_fetchers()
+    """
+    This patch allows DataLoader to correctly process batches that contain multiple bins
+    of packed sequences.
+    """
+    # pylint: disable=global-statement
+    global _ORIGINAL_MAP_DATASET_FETCHER, _ORIGINAL_WORKER_LOOP, _IS_PATCHED
+
+    if _IS_PATCHED:
+        return
+
+    # Store original implementations
+    _ORIGINAL_MAP_DATASET_FETCHER = torch.utils.data._utils.fetch._MapDatasetFetcher
+    _ORIGINAL_WORKER_LOOP = torch.utils.data._utils.worker._worker_loop
+
+    # Apply patches
+    patch_fetchers()
+    torch.utils.data._utils.worker._worker_loop = patched_worker_loop
+
+    _IS_PATCHED = True
+
+
+def remove_multipack_dataloader_patch():
+    """Remove the monkeypatch and restore original PyTorch DataLoader behavior."""
+    # pylint: disable=global-statement
+    global _IS_PATCHED
+
+    if not _IS_PATCHED:
+        return
+
+    if _ORIGINAL_MAP_DATASET_FETCHER:
+        torch.utils.data._utils.fetch._MapDatasetFetcher = _ORIGINAL_MAP_DATASET_FETCHER
+        torch.utils.data.dataloader._utils.fetch._MapDatasetFetcher = (
+            _ORIGINAL_MAP_DATASET_FETCHER
+        )
+
+    if _ORIGINAL_WORKER_LOOP:
+        torch.utils.data._utils.worker._worker_loop = _ORIGINAL_WORKER_LOOP
+
+    _IS_PATCHED = False

src/axolotl/monkeypatch/fsdp2_qlora.py

@@ -9,73 +9,12 @@ Params4bit parameters.
 import importlib
 import inspect
 
-import torch
-from torch.nn import Parameter
-
 from axolotl.monkeypatch.utils import detab_code
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
 
 
-def patched_torch_function(cls, func, types, args=(), kwargs=None):
-    """
-    Patched version of Params4bit.__torch_function__ for preserving Params4bit
-    class identity and attributes.
-    """
-    if kwargs is None:
-        kwargs = {}
-
-    if func in [torch.chunk, torch.split]:
-        tensor = args[0]
-        result = Parameter.__torch_function__(func, types, args, kwargs)
-
-        if isinstance(result, tuple):
-            return tuple(
-                cls(
-                    data=chunk,
-                    requires_grad=tensor.requires_grad,
-                    quant_state=tensor.quant_state,
-                    blocksize=tensor.blocksize,
-                    compress_statistics=tensor.compress_statistics,
-                    quant_type=tensor.quant_type,
-                    quant_storage=tensor.quant_storage,
-                    module=tensor.module,
-                    bnb_quantized=tensor.bnb_quantized,
-                )
-                for chunk in result
-            )
-
-        return cls(
-            data=result,
-            requires_grad=tensor.requires_grad,
-            quant_state=tensor.quant_state,
-            blocksize=tensor.blocksize,
-            compress_statistics=tensor.compress_statistics,
-            quant_type=tensor.quant_type,
-            quant_storage=tensor.quant_storage,
-            module=tensor.module,
-            bnb_quantized=tensor.bnb_quantized,
-        )
-
-    return Parameter.__torch_function__(func, types, args, kwargs)
-
-
-# pylint: disable=protected-access
-def apply_bnb_torch_function_patch():
-    """
-    Patch Params4bit.__torch_function__ using Axolotl-style approach.
-
-    Returns:
-        True if patching succeeded, False otherwise.
-    """
-    from bitsandbytes.nn.modules import Params4bit
-
-    Params4bit.__torch_function__ = classmethod(patched_torch_function)
-
-    LOG.info("Successfully patched Params4bit.__torch_function__")
-
-
 # pylint: disable=protected-access
 def apply_init_sharded_param_patch():
     """Apply patch to FSDPParam._init_sharded_param to support Params4bit."""

src/axolotl/monkeypatch/ring_attn/adapters/batch.py

@@ -20,12 +20,15 @@ from ring_flash_attn import ring_flash_attn_func
 from ring_flash_attn.adapters.hf_adapter import check_params
 from transformers.modeling_flash_attention_utils import is_flash_attn_greater_or_equal
 
-try:
+try:  # pylint: disable=duplicate-code
     from transformers.modeling_flash_attention_utils import _flash_supports_window
 except ImportError:
-    from transformers.modeling_flash_attention_utils import (
+    try:
-        _flash_supports_window_size as _flash_supports_window,
+        from transformers.modeling_flash_attention_utils import (
-    )
+            _flash_supports_window_size as _flash_supports_window,
+        )
+    except ImportError:
+        _flash_supports_window = True
 
 from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
 

src/axolotl/monkeypatch/ring_attn/patch.py

@@ -15,12 +15,15 @@ import torch
 import torch.distributed as dist
 from torch.distributed import DeviceMesh
 
-try:
+try:  # pylint: disable=duplicate-code
     from transformers.modeling_flash_attention_utils import _flash_supports_window
 except ImportError:
-    from transformers.modeling_flash_attention_utils import (
+    try:
-        _flash_supports_window_size as _flash_supports_window,
+        from transformers.modeling_flash_attention_utils import (
-    )
+            _flash_supports_window_size as _flash_supports_window,
+        )
+    except ImportError:
+        _flash_supports_window = True
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
 from axolotl.utils.logging import get_logger

src/axolotl/processing_strategies.py

@@ -6,7 +6,7 @@ from typing import Optional
 from PIL import Image, ImageOps
 from PIL.Image import Resampling
 from torch import Tensor, zeros_like
-from transformers import ProcessorMixin, VoxtralProcessor
+from transformers import ProcessorMixin, SmolVLMProcessor, VoxtralProcessor
 from transformers.image_utils import load_image
 
 from axolotl.utils.dict import remove_none_values
@@ -138,7 +138,7 @@ class ProcessingStrategy:
                     image_key = key
                     break
 
-            # if the image key exists, add the image to the first message
+            # if the image key exists, add the image to the first user message
             if image_key is not None and processed_example[image_key] is not None:
                 # TODO: check if it's normal to be single image only for common datasets
                 # From observation, it's usually a list of single image but some datasets may have several columns for images
@@ -179,26 +179,34 @@ class ProcessingStrategy:
 
                 # Look for any image type in the first message
                 # some dataset have an {type: "image"} in the first message
+                msg_ind_to_add = None
                 ind_to_add = None
+                first_user_idx = None
 
-                for i, content in enumerate(
+                for msg_idx, msg_content in enumerate(processed_example["messages"]):
-                    processed_example["messages"][0]["content"]
+                    if first_user_idx is None and msg_content["role"] == "user":
-                ):
+                        first_user_idx = msg_idx
-                    # Usually datasets created with image columns, don't have it in the messages itself
+                    for i, content in enumerate(
-                    if content["type"] == "image" and all(
+                        processed_example["messages"][msg_idx]["content"]
-                        k not in content for k in ["image", "url", "path", "base64"]
                     ):
-                        ind_to_add = i
+                        # Usually datasets created with image columns, don't have it in the messages itself
-                        break
+                        if content["type"] == "image" and all(
+                            k not in content for k in ["image", "url", "path", "base64"]
+                        ):
+                            msg_ind_to_add = msg_idx
+                            ind_to_add = i
+                            break
 
                 # If an image type is found, add the image to that index
-                if ind_to_add is not None:
+                if ind_to_add is not None and msg_ind_to_add is not None:
-                    processed_example["messages"][0]["content"][ind_to_add][
+                    processed_example["messages"][msg_ind_to_add]["content"][
-                        "image"
+                        ind_to_add
-                    ] = image_value
+                    ]["image"] = image_value
                 else:
-                    # if no image type is found, add it to end of the first message
+                    # if no image type is found, add it to end of the first user message
-                    processed_example["messages"][0]["content"].append(
+                    if first_user_idx is None:
+                        first_user_idx = 0
+                    processed_example["messages"][first_user_idx]["content"].append(
                         {
                             "type": "image",
                             "image": image_value,
@@ -395,6 +403,24 @@ class VoxtralProcessingStrategy(ProcessingStrategy):
         return labels
 
 
+class SmolVLM2ProcessingStrategy(ProcessingStrategy):
+    """Processing Strategy class for SmolVLM2"""
+
+    def __init__(
+        self,
+        processor: ProcessorMixin,
+        chat_template: Optional[str] = None,
+        image_size: int | tuple[int, int] | None = None,
+        image_resize_algorithm: Resampling | None = None,
+    ):
+        super().__init__(processor, chat_template, image_size, image_resize_algorithm)
+        self.image_token = "<image>"  # nosec
+
+        self.image_token_id = processor.tokenizer.additional_special_tokens_ids[
+            processor.tokenizer.additional_special_tokens.index(self.image_token)
+        ]
+
+
 def get_processing_strategy(
     processor: ProcessorMixin,
     chat_template,
@@ -402,32 +428,43 @@ def get_processing_strategy(
     image_size: int | tuple[int, int] | None = None,
     image_resize_algorithm: Resampling | None = None,
 ):
+    processing_kwargs = {
+        "processor": processor,
+        "chat_template": chat_template,
+        "image_size": image_size,
+        "image_resize_algorithm": image_resize_algorithm,
+    }
+
+    if chat_template_type in [None, "tokenizer_default"] and hasattr(
+        processor.tokenizer, "chat_template"
+    ):
+        processing_kwargs["chat_template"] = processor.tokenizer.chat_template
+
     if chat_template_type == "qwen2_vl":
         return Qwen2VLProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
+            **processing_kwargs,
         )
     if chat_template_type == "gemma3":
         return Gemma3ProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
+            **processing_kwargs,
         )
     if chat_template_type == "gemma3n":
         return Gemma3nProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
+            **processing_kwargs,
-        )
-    if chat_template_type in [
-        "llama3_2_vision",
-        "llama4",
-        "llava",
-        "mistral_v7_tekken",
-        "pixtral",
-    ]:
-        return ProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
         )
 
     if isinstance(processor, VoxtralProcessor):
         return VoxtralProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
+            **processing_kwargs,
         )
 
-    raise ValueError(f"Unsupported chat template type: {chat_template_type}")
+    if isinstance(processor, SmolVLMProcessor):
+        return SmolVLM2ProcessingStrategy(
+            **processing_kwargs,
+        )
+
+    # llama3_2_vision, llama4, llava
+    # mistral_v7_tekken, pixtral, lfm2vl
+    return ProcessingStrategy(
+        **processing_kwargs,
+    )

src/axolotl/prompt_strategies/chat_template.py

@@ -129,13 +129,21 @@ class ChatTemplatePrompter(Prompter):
                 images=images,
                 return_tensors="pt",
             )
+            if hasattr(batch, "to_dict"):
+                batch = batch.to_dict()
+            else:
+                batch = dict(batch)
+
             # workaround since processor works in batches instead of single examples
+            out = {}
             for k, val in batch.items():
-                if k in ["pixel_values"]:
+                if hasattr(val, "tolist"):
-                    batch[k] = val.tolist()
+                    out[k] = (
+                        val.tolist() if k == "pixel_values" else val.squeeze(0).tolist()
+                    )
                 else:
-                    batch[k] = val.squeeze().tolist()
+                    out[k] = val
-            return batch
+            return out
 
         return self.tokenizer.apply_chat_template(
             conversation,
@@ -433,10 +441,13 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 tokenized_prompt["attention_mask"] = [1] * len(input_ids)
             else:
                 input_ids = tokenized_res["input_ids"]
-                tokenized_prompt = tokenized_res
+                tokenized_prompt = dict(tokenized_res)
 
             if not self.train_on_inputs:
-                user_prompt_len = len(prompt_ids)
+                if isinstance(prompt_ids, dict):
+                    user_prompt_len = len(prompt_ids["input_ids"])
+                else:
+                    user_prompt_len = len(prompt_ids)
                 labels = [-100] * user_prompt_len + input_ids[user_prompt_len:]
             else:
                 labels = input_ids

src/axolotl/train.py

@@ -4,11 +4,14 @@ from __future__ import annotations
 
 import importlib
 import inspect
+import json
 import os
+import shutil
 import signal
 import sys
 import typing
 import weakref
+from collections import OrderedDict
 from contextlib import ExitStack
 from pathlib import Path
 from typing import Any, Dict
@@ -38,6 +41,7 @@ from axolotl.utils.distributed import cleanup_distributed
 from axolotl.utils.freeze import freeze_layers_except
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.enums import RLType
+from axolotl.utils.train import determine_last_checkpoint
 from axolotl.utils.trainer import setup_trainer
 
 try:
@@ -46,7 +50,7 @@ except ImportError:
     BetterTransformer = None
 
 if typing.TYPE_CHECKING:
-    from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
+    from axolotl.core.builders import HFCausalTrainerBuilder, HFRLTrainerBuilder
 
 LOG = get_logger(__name__)
 
@@ -124,32 +128,6 @@ def setup_reference_model(
     return model_ref
 
 
-def determine_resume_checkpoint(cfg: DictDefault) -> str | None:
-    """
-    Determine the checkpoint to resume from based on configuration.
-
-    Args:
-        cfg: Dictionary mapping `axolotl` config keys to values.
-
-    Returns:
-        Path to the checkpoint to resume from, or `None` if not resuming.
-    """
-    if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
-        possible_checkpoints = [
-            str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
-        ]
-        if len(possible_checkpoints) > 0:
-            sorted_paths = sorted(
-                possible_checkpoints,
-                key=lambda path: int(path.split("-")[-1]),
-            )
-            cfg.resume_from_checkpoint = sorted_paths[-1]
-            LOG.info(
-                f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
-            )
-    return cfg.resume_from_checkpoint
-
-
 def setup_signal_handler(
     cfg: DictDefault, model: PreTrainedModel, safe_serialization: bool
 ):
@@ -275,19 +253,60 @@ def save_trained_model(
             # final model weights have already been saved by `ReLoRACallback.on_train_end`
             return
 
-    if trainer.is_fsdp_enabled or cfg.fsdp_config:
+    if (  # pylint: disable=too-many-nested-blocks
+        trainer.is_fsdp_enabled or cfg.fsdp_config
+    ):
         if cfg.fsdp_config or cfg.fsdp:
             if cfg.fsdp_config.final_state_dict_type:
                 state_dict_type = cfg.fsdp_config.final_state_dict_type
             else:
                 state_dict_type = cfg.fsdp_config.state_dict_type
             trainer.accelerator.state.fsdp_plugin.set_state_dict_type(state_dict_type)
-        trainer.save_model(cfg.output_dir)
+        trainer.save_model(cfg.output_dir)  # only handles FULL_STATE_DICT
         if state_dict_type == "SHARDED_STATE_DICT":
             LOG.info(
                 "The final model was saved with a sharded state dict. Please ensure you merge "
                 "the sharded weights with `merge-sharded-fsdp-weights`."
             )
+            checkpoint_dir = determine_last_checkpoint(cfg, update=False)
+            if (
+                not (Path(cfg.output_dir) / "model.safetensors.index.json").exists()
+                and checkpoint_dir
+            ):
+                # import here to prevent circular import
+                from axolotl.cli.merge_sharded_fsdp_weights import merge_fsdp_weights
+
+                fsdp_dir = Path(checkpoint_dir) / "pytorch_model_fsdp_0"
+                merged_path = str(Path(cfg.output_dir) / "merged")
+                merge_fsdp_weights(
+                    checkpoint_dir=str(fsdp_dir),
+                    output_path=merged_path,
+                    safe_serialization=True,
+                )
+                trainer.accelerator.wait_for_everyone()
+                if trainer.accelerator.is_main_process:
+                    # move all files in merged_path to cfg.output_dir
+                    for merged_file in Path(merged_path).iterdir():
+                        if (Path(cfg.output_dir) / merged_file.name).exists():
+                            (Path(cfg.output_dir) / merged_file.name).unlink()
+                        shutil.move(str(merged_file), cfg.output_dir)
+                    shutil.rmtree(merged_path)  # remove what should be an empty dir
+        # TODO(wing):see https://github.com/huggingface/transformers/pull/40207
+        # cleanup the FSDP prefix in the model config.json
+        if trainer.accelerator.is_main_process:
+            with open(
+                Path(cfg.output_dir) / "config.json", "r", encoding="utf-8"
+            ) as config_file_io:
+                # read the model config as an OrderedDict
+                config = json.load(config_file_io, object_pairs_hook=OrderedDict)
+                config["architectures"] = [
+                    name.lstrip("FSDP") for name in config["architectures"]
+                ]
+            # write the updated model config back
+            with open(
+                os.path.join(cfg.output_dir, "config.json"), "w", encoding="utf-8"
+            ) as config_file_io:
+                json.dump(config, config_file_io, indent=2)
     elif cfg.deepspeed and is_deepspeed_zero3_enabled():
         # Copied over from: https://github.com/huggingface/accelerate/blob/5ae611118057232f441055f7ef9ba0b0f2b8d533/docs/source/usage_guides/deepspeed.md#saving-and-loading
         trainer.accelerator.wait_for_everyone()
@@ -564,7 +583,7 @@ def train(
     setup_model_card(cfg)
 
     # Execute the training
-    resume_from_checkpoint = determine_resume_checkpoint(cfg)
+    resume_from_checkpoint = determine_last_checkpoint(cfg)
     execute_training(cfg, trainer, resume_from_checkpoint)
 
     # clear cache

src/axolotl/utils/collators/mm_chat.py

@@ -5,7 +5,6 @@ Collators for multi-modal chat messages and packing
 from dataclasses import dataclass
 from typing import Any, Optional, Union
 
-import torch
 from torch import Tensor
 from transformers import PreTrainedTokenizerBase
 from transformers.data.data_collator import DataCollatorMixin
@@ -42,62 +41,19 @@ class MultiModalChatDataCollator(DataCollatorMixin):
         examples = self.processing_strategy(examples)
 
         # Initialize batch
-        batch: dict[str, Any] = {}
+        messages = [ex["messages"] for ex in examples]
 
-        # Process each example
+        batch = self.processing_strategy.processor.apply_chat_template(
-        for example in examples:
+            messages,
-            # Apply chat template to process the example
+            add_generation_prompt=False,
-            # This method requires transformers>=4.49.0
+            tokenize=True,
-            result = self.processing_strategy.processor.apply_chat_template(
+            return_tensors="pt",
-                example["messages"],
+            padding=True,
-                add_generation_prompt=False,
+            return_dict=True,
-                tokenize=True,
+            chat_template=self.processing_strategy.chat_template,
-                return_tensors="pt",
-                padding=True,
-                return_dict=True,
-                chat_template=self.processing_strategy.chat_template,
-            )
-
-            # TODO: Check if need handling for len(input_ids) > sequence_len
-
-            # Add the processed tensors to our batch
-            for key in result.keys():
-                if key not in batch:
-                    batch[key] = []
-
-                batch[key].append(result[key].squeeze(0))
-
-        # Pad sequences to the same length
-        input_ids = torch.nn.utils.rnn.pad_sequence(
-            batch["input_ids"],
-            batch_first=True,
-            padding_value=self.tokenizer.pad_token_id,
         )
 
-        attention_mask = torch.nn.utils.rnn.pad_sequence(
-            batch["attention_mask"], batch_first=True, padding_value=0
-        )
-
-        # Create the final batch
-        final_batch = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-
-        for key, val in batch.items():
-            if key in ["input_ids", "attention_mask"]:
-                continue
-
-            if key in ["token_type_ids", "cross_attention_mask"]:
-                final_batch[key] = torch.nn.utils.rnn.pad_sequence(
-                    val, batch_first=True, padding_value=0
-                )
-            else:
-                final_batch[key] = torch.stack(val)
-
         # Process the labels
-        final_batch["labels"] = self.processing_strategy.process_labels(
+        batch["labels"] = self.processing_strategy.process_labels(batch["input_ids"])
-            final_batch["input_ids"]
-        )
 
-        return final_batch
+        return batch

src/axolotl/utils/data/sft.py

@@ -28,7 +28,7 @@ from axolotl.utils.data.shared import (
 )
 from axolotl.utils.data.utils import (
     deduplicate_and_log_datasets,
-    drop_long_seq_in_dataset,
+    handle_long_seq_in_dataset,
     retry_on_request_exceptions,
 )
 from axolotl.utils.data.wrappers import get_dataset_wrapper
@@ -339,9 +339,9 @@ def _load_raw_datasets(
 
     if not cfg.skip_prepare_dataset:
         if split == "test" and cfg.eval_sequence_len:
-            dataset = drop_long_seq_in_dataset(dataset, cfg.eval_sequence_len, cfg)
+            dataset = handle_long_seq_in_dataset(dataset, cfg.eval_sequence_len, cfg)
         else:
-            dataset = drop_long_seq_in_dataset(dataset, cfg.sequence_len, cfg)
+            dataset = handle_long_seq_in_dataset(dataset, cfg.sequence_len, cfg)
         if cfg.sample_packing:
             dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 

src/axolotl/utils/data/utils.py

@@ -148,7 +148,36 @@ def deduplicate_and_log_datasets(
     return dataset, other_dataset
 
 
-def drop_long_seq_in_dataset(
+def truncate_long_seq(sample, sequence_len=2048, min_sequence_len=2):
+    """
+    Truncate samples whose sequence length is too long (> sequence_len)
+    or drop those too short (< min_sequence_len).
+    """
+    min_sequence_len = min_sequence_len or 2
+
+    input_ids = sample["input_ids"]
+    results = []
+
+    # Batched (input_ids is a list of lists)
+    for i, seq in enumerate(input_ids):
+        length = len(seq)
+        if length < min_sequence_len:
+            results.append(False)
+        elif length > sequence_len:
+            sample["input_ids"][i] = seq[:sequence_len]
+            if "attention_mask" in sample:
+                sample["attention_mask"][i] = sample["attention_mask"][i][:sequence_len]
+            if "labels" in sample:
+                sample["labels"][i] = sample["labels"][i][:sequence_len]
+            if "position_ids" in sample:
+                sample["position_ids"][i] = sample["position_ids"][i][:sequence_len]
+            results.append(True)
+        else:
+            results.append(True)
+    return results
+
+
+def handle_long_seq_in_dataset(
     dataset: Dataset, sequence_len: int, cfg: DictDefault
 ) -> Dataset:
     """Remove sequences longer than configured maximum from dataset.
@@ -192,8 +221,21 @@ def drop_long_seq_in_dataset(
     if filter_map_kwargs:
         drop_long_kwargs["desc"] = f"Dropping Long Sequences (>{sequence_len})"
 
+    excess_length_strategy = (cfg.excess_length_strategy or "drop").lower()
+    if excess_length_strategy == "truncate":
+        process_fn = functools.partial(
+            truncate_long_seq,
+            sequence_len=sequence_len,
+            min_sequence_len=cfg.min_sample_len,
+        )
+        drop_long_kwargs["desc"] = (
+            f"Truncating/Filtering Sequences (target_len={sequence_len})"
+        )
+    else:
+        process_fn = drop_long
+
     dataset = dataset.filter(
-        drop_long,
+        process_fn,
         batched=True,
         **filter_map_kwargs,
         **drop_long_kwargs,
@@ -201,6 +243,11 @@ def drop_long_seq_in_dataset(
     if prior_len:
         dropped = prior_len - len(dataset)
         if dropped:
-            LOG.warning(f"Dropped {dropped} long samples from dataset")
+            action = (
+                "truncated/filtered"
+                if excess_length_strategy == "truncate"
+                else "dropped"
+            )
+            LOG.warning(f"{action.title()} {dropped} samples from dataset")
 
     return dataset

src/axolotl/utils/schemas/config.py

@@ -414,6 +414,12 @@ class AxolotlInputConfig(
             "description": "The maximum length of an input to train with, this should typically be less than 2048 as most models have a token/context limit of 2048"
         },
     )
+    excess_length_strategy: Literal["drop", "truncate"] | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "What to do when a tokenized row exceeds sequence_len. 'drop' removes the row; 'truncate' slices tensors to sequence_len. Defaults to 'drop' for backward compatibility."
+        },
+    )
     eval_sequence_len: int | None = Field(
         default=None,
         json_schema_extra={
@@ -453,6 +459,12 @@ class AxolotlInputConfig(
             "description": "The multiprocessing start method to use for packing. Should be 'fork', 'spawn' or 'forkserver'"
         },
     )
+    squash_position_ids: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Whether to squash position_ids for packing, effectively extending context length."
+        },
+    )
     eval_sample_packing: bool | None = Field(
         default=None,
         json_schema_extra={

src/axolotl/utils/schemas/validation.py

@@ -3,6 +3,7 @@
 # pylint: disable=too-many-boolean-expressions
 
 import json
+import sys
 import tempfile
 from pathlib import Path
 
@@ -369,10 +370,10 @@ class TrainingValidationMixin:
                 "see speed improvements. Please consider setting `torch_compile: "
                 "true` in your config."
             )
+        fsdp_config = data.get("fsdp_config") or {}
         if data.get("fp8") and (
-            data.get("fsdp_config", {}).get("activation_checkpointing", False) is True
+            fsdp_config.get("activation_checkpointing", False) is True
-            or data.get("fsdp_config", {}).get("fsdp_activation_checkpointing", False)
+            or fsdp_config.get("fsdp_activation_checkpointing", False) is True
-            is True
         ):
             LOG.warning(
                 "FP8 + FSDP2 + activation checkpointing may be slower than BF16 "
@@ -817,13 +818,13 @@ class OptimizationValidationMixin:
     @model_validator(mode="before")
     @classmethod
     def check_fsdp_version_in_fsdp_config(cls, data):
-        if data.get("fsdp_config"):
+        fsdp_config = data.get("fsdp_config") or {}
-            if data.get("fsdp_config", {}).get("fsdp_version"):
+        if fsdp_config and fsdp_config.get("fsdp_version"):
-                LOG.warning(
+            LOG.warning(
-                    "Configuring `fsdp_version` in `fsdp_config` is deprecated. "
+                "Configuring `fsdp_version` in `fsdp_config` is deprecated. "
-                    "Please configure `fsdp_version` as a top-level field."
+                "Please configure `fsdp_version` as a top-level field."
-                )
+            )
-                data["fsdp_version"] = data.get("fsdp_config").pop("fsdp_version")
+            data["fsdp_version"] = fsdp_config.pop("fsdp_version")
         return data
 
     @model_validator(mode="before")
@@ -1151,10 +1152,8 @@ class ModelCompatibilityValidationMixin:
     @classmethod
     def check_gpt_oss_fsdp_loading(cls, data):
         if data.get("model_quantization_config", "") == "Mxfp4Config":
-            if (
+            fsdp_config = data.get("fsdp_config") or {}
-                data.get("fsdp_config", {}).get("cpu_ram_efficient_loading", False)
+            if fsdp_config.get("cpu_ram_efficient_loading", False) is True:
-                is True
-            ):
                 raise ValueError(
                     "FSDP cpu_ram_efficient_loading is not supported for Mxfp4Config model quantization."
                 )
@@ -1251,10 +1250,26 @@ class ComplexValidationMixin:
 
             try:
                 import transformers.modeling_flash_attention_utils
+                from transformers.utils import is_flash_attn_greater_or_equal
 
                 # pylint: disable=protected-access
-                transformers.modeling_flash_attention_utils._flash_supports_window_size = (
+                transformers.modeling_flash_attention_utils._flash_supports_window = (
-                    transformers.modeling_flash_attention_utils._flash_supports_window
+                    True
+                )
+                setattr(
+                    sys.modules["transformers.modeling_flash_attention_utils"],
+                    "_flash_supports_window",
+                    True,
+                )
+                setattr(
+                    sys.modules["transformers.modeling_flash_attention_utils"],
+                    "_flash_supports_window_size",
+                    True,
+                )
+                setattr(
+                    sys.modules["transformers.modeling_flash_attention_utils"],
+                    "is_flash_attn_greater_or_equal",
+                    is_flash_attn_greater_or_equal,
                 )
                 import ring_flash_attn  # noqa: F401 # pylint:disable=unused-import
             except ImportError as exception:

src/axolotl/utils/train.py

@@ -0,0 +1,45 @@
+"""Training utils for checkpoints"""
+
+from pathlib import Path
+
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+
+def determine_last_checkpoint(cfg: DictDefault, update: bool = True) -> str | None:
+    """
+    Determine the checkpoint to resume from based on configuration.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        update: Whether to update the config with the determined checkpoint
+
+    Returns:
+        Path to the checkpoint to resume from, or `None` if not resuming.
+    """
+    last_checkpoint = None
+    checkpoints = sorted(
+        (
+            p
+            for p in Path(cfg.output_dir).glob("checkpoint-*")
+            if p.name.split("-")[-1].isdigit()
+        ),
+        key=lambda p: int(p.name.split("-")[-1]),
+    )
+    if checkpoints:
+        last_checkpoint = str(checkpoints[-1])
+        if not update:
+            return last_checkpoint
+
+    if (
+        cfg.resume_from_checkpoint is None
+        and cfg.auto_resume_from_checkpoints
+        and last_checkpoint is not None
+    ):
+        cfg.resume_from_checkpoint = last_checkpoint
+        LOG.info(
+            f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
+        )
+    return cfg.resume_from_checkpoint

tests/e2e/patched/test_fsdp2_qlora.py

@@ -1,126 +1,28 @@
-"""Integration tests for FSDP Params4bit patches."""
+"""Integration tests for FSDP2 Params4bit patches."""
 
-from unittest.mock import Mock, patch
-
-import bitsandbytes as bnb
 import pytest
-import torch
 from torch.distributed.fsdp._fully_shard._fsdp_param import FSDPParam
 
-from axolotl.monkeypatch.fsdp2_qlora import (
-    apply_bnb_torch_function_patch,
-    patched_torch_function,
-)
-
-
-@pytest.fixture
-def mock_params4bit():
-    """Create a mock Params4bit instance with test attributes."""
-    mock_instance = Mock()
-    mock_instance.requires_grad = True
-    mock_instance.quant_state = "test_state"
-    mock_instance.blocksize = 128
-    mock_instance.compress_statistics = True
-    mock_instance.quant_type = "fp4"
-    mock_instance.quant_storage = "test_storage"
-    mock_instance.module = "test_module"
-    mock_instance.bnb_quantized = True
-    return mock_instance
-
-
-class TestBnbTorchFunctionPatch:
-    """Test the Params4bit.__torch_function__ patch."""
-
-    def test_apply_patch(self):
-        """Test that the patch can be applied."""
-        with patch("bitsandbytes.nn.modules.Params4bit") as mock_cls:
-            apply_bnb_torch_function_patch()
-            assert hasattr(mock_cls, "__torch_function__")
-            assert isinstance(mock_cls.__torch_function__, classmethod)
-
-    # pylint: disable=redefined-outer-name
-    def test_torch_chunk_preserves_attributes(self, mock_params4bit):
-        """Test that torch.chunk preserves Params4bit attributes."""
-        mock_cls = Mock()
-        chunks = (torch.tensor([1, 2]), torch.tensor([3, 4]))
-
-        with patch("torch.nn.Parameter.__torch_function__", return_value=chunks):
-            result = patched_torch_function(
-                mock_cls,
-                torch.chunk,
-                (type(mock_params4bit),),
-                args=(mock_params4bit, 2),
-            )
-
-            assert isinstance(result, tuple)
-            assert len(result) == 2
-
-            # Check that Params4bit constructor was called with preserved attributes
-            assert mock_cls.call_count == 2
-            for call in mock_cls.call_args_list:
-                kwargs = call[1]
-                assert kwargs["requires_grad"] == mock_params4bit.requires_grad
-                assert kwargs["quant_state"] == mock_params4bit.quant_state
-                assert kwargs["blocksize"] == mock_params4bit.blocksize
-
-    # pylint: disable=redefined-outer-name
-    def test_other_functions_fallback(self, mock_params4bit):
-        """Test that non-chunk/split functions use Parameter fallback."""
-        mock_cls = Mock()
-        fallback_result = torch.tensor([5, 6, 7])
-
-        with patch(
-            "torch.nn.Parameter.__torch_function__", return_value=fallback_result
-        ) as mock_fallback:
-            result = patched_torch_function(
-                mock_cls, torch.add, (type(mock_params4bit),), args=(mock_params4bit, 1)
-            )
-
-            # Should call Parameter.__torch_function__ and return its result
-            mock_fallback.assert_called_once()
-            assert result is fallback_result
-            mock_cls.assert_not_called()
-
 
 class TestFSDPPatchIntegration:
     """Test FSDP patch integration."""
 
     @pytest.mark.integration
-    def test_all_patches_together(self):
+    def test_fsdp2_init_patches(self):
         """Test that all patches can be applied together."""
         from axolotl.monkeypatch.fsdp2_qlora import (
             apply_init_sharded_param_patch,
             apply_init_unsharded_param_patch,
         )
 
-        # Store original methods before patching
-        original_torch_function = getattr(
-            bnb.nn.modules.Params4bit, "__torch_function__", None
-        )
-
         # pylint: disable=protected-access
         original_init_sharded = FSDPParam._init_sharded_param
         original_init_unsharded = FSDPParam.init_unsharded_param
 
         # Apply patches
-        apply_bnb_torch_function_patch()
         apply_init_sharded_param_patch()
         apply_init_unsharded_param_patch()
 
-        # Verify patches were applied
-        current_torch_function = getattr(
-            bnb.nn.modules.Params4bit, "__torch_function__", None
-        )
-        if original_torch_function is not None:
-            assert (
-                current_torch_function != original_torch_function
-            ), "Params4bit.__torch_function__ was not patched"
-        else:
-            assert (
-                current_torch_function is not None
-            ), "Params4bit.__torch_function__ was not added"
-
-        # Check that FSDP methods were patched
         assert (
             # pylint: disable=protected-access
             FSDPParam._init_sharded_param

tests/e2e/utils.py

@@ -147,7 +147,11 @@ def require_hopper(test_case):
 
 
 def check_tensorboard(
-    temp_run_dir: str, tag: str, lt_val: float, assertion_err: str
+    temp_run_dir: str,
+    tag: str,
+    lt_val: float,
+    assertion_err: str,
+    rtol: float = 0.02,
 ) -> None:
     """
     helper function to parse and check tensorboard logs
@@ -157,6 +161,7 @@ def check_tensorboard(
     reader = SummaryReader(event_file)
     df = reader.scalars  # pylint: disable=invalid-name
     df = df[(df.tag == tag)]  # pylint: disable=invalid-name
+    lt_val = (1 + rtol) * lt_val
     if "%s" in assertion_err:
         assert df.value.values[-1] < lt_val, assertion_err % df.value.values[-1]
     else:

tests/test_packed_batch_sampler.py

@@ -8,7 +8,7 @@ from transformers import AutoTokenizer
 from axolotl.datasets import TokenizedPromptDataset
 from axolotl.prompt_strategies.completion import load
 from axolotl.utils.collators import V2BatchSamplerDataCollatorForSeq2Seq
-from axolotl.utils.data.utils import drop_long_seq_in_dataset
+from axolotl.utils.data.utils import handle_long_seq_in_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
@@ -48,7 +48,13 @@ class TestBatchedSamplerPacking:
         max_seq_length,
         sequential,
     ):
-        import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
+        from axolotl.monkeypatch.data.batch_dataset_fetcher import (
+            apply_multipack_dataloader_patch,
+            remove_multipack_dataloader_patch,
+        )
+
+        # Apply the patch for multipack handling
+        apply_multipack_dataloader_patch()
 
         dataset = dataset_winglian_tiny_shakespeare["train"]
 
@@ -70,7 +76,7 @@ class TestBatchedSamplerPacking:
         )
         train_dataset = concatenate_datasets([dataset_wrapper])
 
-        train_dataset = drop_long_seq_in_dataset(train_dataset, cfg.sequence_len, cfg)
+        train_dataset = handle_long_seq_in_dataset(train_dataset, cfg.sequence_len, cfg)
 
         lengths = get_dataset_lengths(train_dataset)
         batch_sampler = MultipackBatchSampler(
@@ -101,10 +107,14 @@ class TestBatchedSamplerPacking:
             for pack in batch:
                 batch_idxs.extend(pack)
 
-        for batch in loader:
+        try:
-            assert batch["input_ids"].numel() <= batch_size * max_seq_length
+            for batch in loader:
-            assert batch["input_ids"].shape[1] == max_seq_length
+                assert batch["input_ids"].numel() <= batch_size * max_seq_length
+                assert batch["input_ids"].shape[1] == max_seq_length
 
-        original_idxs = set(range(len(train_dataset)))
+            original_idxs = set(range(len(train_dataset)))
-        assert original_idxs == set(batch_idxs)
+            assert original_idxs == set(batch_idxs)
-        assert len(batch_idxs) == len(set(batch_idxs))
+            assert len(batch_idxs) == len(set(batch_idxs))
+        finally:
+            # Clean up: remove the patch after the test
+            remove_multipack_dataloader_patch()

tests/utils/test_train.py

@@ -0,0 +1,24 @@
+"""test for train checkpoint utils"""
+
+import os
+
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.train import determine_last_checkpoint
+
+
+def test_determine_last_checkpoint(temp_dir):
+    cfg = DictDefault(
+        output_dir=temp_dir,
+    )
+    for cpt_idx in [1, 9, 10, 20]:
+        os.makedirs(
+            os.path.join(cfg.output_dir, f"checkpoint-{cpt_idx}"), exist_ok=True
+        )
+
+    last_checkpoint = determine_last_checkpoint(cfg, update=False)
+    assert last_checkpoint == os.path.join(cfg.output_dir, "checkpoint-20")
+
+    cfg.resume_from_checkpoint = None
+    cfg.auto_resume_from_checkpoints = True
+    determine_last_checkpoint(cfg, update=True)
+    assert cfg.resume_from_checkpoint == os.path.join(cfg.output_dir, "checkpoint-20")