add depr warning for preprocess --iterable

* upgrade peft to 0.17.1

* upgrade for transformers too

.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/main.yml

@@ -98,6 +98,12 @@ jobs:
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras:
+            is_latest:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras: vllm
             is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
@@ -151,6 +157,18 @@ jobs:
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras:
+            is_latest:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras: vllm
+            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/tests.yml

@@ -105,7 +105,8 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/ --cov=axolotl --cov-report=xml
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
+          pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
           pytest -v --durations=10 tests/patched/ --cov=axolotl --cov-append --cov-report=xml
           pytest -v --durations=10 tests/cli/ --cov=axolotl --cov-append --cov-report=xml
 
@@ -179,21 +180,52 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
-          pytest -v --durations=10 tests/patched/
+          pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
           pytest -v --durations=10 tests/cli/
 
       - name: cleanup pip cache
         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
@@ -239,13 +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

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
@@ -23,7 +23,7 @@ repos:
     hooks:
     - id: flake8
 -   repo: https://github.com/pylint-dev/pylint
-    rev: v3.3.7
+    rev: v3.3.8
     hooks:
     - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy

CITATION.cff

@@ -0,0 +1,10 @@
+cff-version: 1.2.0
+type: software
+title: "Axolotl: Post-Training for AI Models"
+message: "If you use this software, please cite it as below."
+authors:
+  - name: "Axolotl maintainers and contributors"
+repository-code: "https://github.com/axolotl-ai-cloud/axolotl"
+url: "https://axolotl.ai/"
+license: Apache-2.0
+date-released: "2023-05-30"

README.md

@@ -149,6 +149,20 @@ Contributions are welcome! Please see our [Contributing Guide](https://github.co
 
 Interested in sponsoring? Contact us at [wing@axolotl.ai](mailto:wing@axolotl.ai)
 
+## 📝 Citing Axolotl
+
+If you use Axolotl in your research or projects, please cite it as follows:
+
+```bibtex
+@software{axolotl,
+  title = {Axolotl: Post-Training for AI Models},
+  author = {{Axolotl maintainers and contributors}},
+  url = {https://github.com/axolotl-ai-cloud/axolotl},
+  license = {Apache-2.0},
+  year = {2023}
+}
+```
+
 ## 📜 License
 
 This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.

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/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.12.0"
+__version__ = "0.13.0.dev"

src/axolotl/cli/args.py

@@ -14,9 +14,13 @@ class PreprocessCliArgs:
     prompter: Optional[str] = field(default=None)
     download: Optional[bool] = field(default=True)
     iterable: Optional[bool] = field(
-        default=None,
+        default=False,
         metadata={
-            "help": "Use IterableDataset for streaming processing of large datasets"
+            "help": (
+                "[DEPRECATED] No longer supported. For streaming datasets, use "
+                "'axolotl train' and set 'streaming: true' in your YAML config, or "
+                "pass --streaming instead in the CLI."
+            )
         },
     )
 
@@ -40,6 +44,12 @@ 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/config.py

@@ -153,15 +153,14 @@ def prepare_plugins(cfg: DictDefault):
         plugin_manager = PluginManager.get_instance()
         for plugin_name in cfg["plugins"]:
             plugin_manager.register(plugin_name)
+        for plugin in plugin_manager.plugins.values():
+            plugin.register(cfg)
 
 
 def plugin_set_cfg(cfg: DictDefault):
     if cfg.get("plugins"):
         plugin_manager = PluginManager.get_instance()
         plugin_manager.cfg = cfg
-        # now that we have the finalized cfg, register the plugins individually
-        for plugin in plugin_manager.plugins.values():
-            plugin.register(cfg)
 
 
 def load_cfg(

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/main.py

@@ -123,9 +123,10 @@ def train(
     _launcher = None if kwargs.get("use_ray") else launcher
 
     # Process each configuration
-    for cfg_file in generate_config_files(config, sweep):
+    for cfg_file, is_group in generate_config_files(config, sweep):
         try:
-            launch_training(cfg_file, _launcher, cloud, kwargs, launcher_args)
+            use_exec = is_group is not True
+            launch_training(cfg_file, _launcher, cloud, kwargs, launcher_args, use_exec)
         except subprocess.CalledProcessError as exc:
             LOG.error(f"Failed to train/fine-tune config '{cfg_file}': {exc}")
             if not sweep:

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

@@ -35,10 +35,20 @@ def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
     check_accelerate_default_config()
     check_user_token()
 
+    if cli_args.iterable:
+        LOG.error(
+            "The --iterable CLI argument for 'axolotl preprocess' is no longer "
+            "supported. For training, set 'streaming: true' in your YAML config or "
+            "pass '--streaming' in your 'axolotl train' command for on-the-fly "
+            "preprocessing."
+        )
+        return
+
     for key in ["skip_prepare_dataset", "pretraining_dataset"]:
         if cfg.get(key):
             LOG.error(
-                f"You have set `{key}:`. `preprocess` is not needed. Run the `axolotl train` CLI directly instead."
+                f"You have set `{key}:`. `preprocess` is not needed. Run the 'axolotl "
+                "train' CLI directly instead."
             )
             return
 
@@ -97,7 +107,8 @@ 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

@@ -2,7 +2,9 @@
 
 import os
 import subprocess  # nosec
+import sys
 import tempfile
+from pathlib import Path
 from typing import Any, Iterator, Literal
 
 import yaml
@@ -64,10 +66,18 @@ def build_command(base_cmd: list[str], options: dict[str, Any]) -> list[str]:
     return cmd
 
 
-def generate_config_files(config: str, sweep: str | None) -> Iterator[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
+    """
+
     if not sweep:
-        yield config
+        yield config, False
         return
 
     # Load sweep and base configurations
@@ -78,7 +88,13 @@ def generate_config_files(config: str, sweep: str | None) -> Iterator[str]:
 
     # Generate all possible configurations
     permutations = generate_sweep_configs(base_config, sweep_config)
-    for permutation in permutations:
+    is_group = len(permutations) > 1
+    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",
@@ -88,7 +104,7 @@ def generate_config_files(config: str, sweep: str | None) -> Iterator[str]:
         )
         yaml.dump(permutation, temp_file)
         temp_file.close()
-        yield temp_file.name
+        yield temp_file.name, is_group
 
 
 def launch_training(
@@ -97,6 +113,7 @@ def launch_training(
     cloud: str | None,
     kwargs: dict,
     launcher_args: list[str] | None = None,
+    use_exec: bool = False,
 ) -> None:
     """Execute training with the given configuration."""
     launcher_args = launcher_args or []
@@ -105,11 +122,14 @@ def launch_training(
         _launch_cloud_training(cloud, cfg_file, launcher, kwargs, launcher_args)
     elif launcher:
         if launcher == "accelerate":
-            _launch_accelerate_training(cfg_file, kwargs, launcher_args)
+            _launch_accelerate_training(cfg_file, kwargs, launcher_args, use_exec)
         elif launcher == "torchrun":
-            _launch_torchrun_training(cfg_file, kwargs, launcher_args)
+            _launch_torchrun_training(cfg_file, kwargs, launcher_args, use_exec)
         elif launcher == "python":
             _launch_python_training(cfg_file, kwargs)
+    elif launcher is None:
+        # handle ray train launch
+        _launch_python_training(cfg_file, kwargs)
 
 
 def _launch_cloud_training(
@@ -136,7 +156,10 @@ def _launch_cloud_training(
 
 
 def _launch_accelerate_training(
-    cfg_file: str, kwargs: dict, launcher_args: list[str] | None = None
+    cfg_file: str,
+    kwargs: dict,
+    launcher_args: list[str] | None = None,
+    use_exec: bool = False,
 ) -> None:
     """Execute training via accelerate launcher."""
     launcher_args = launcher_args or []
@@ -161,11 +184,20 @@ def _launch_accelerate_training(
         base_cmd.append(cfg_file)
 
     cmd = build_command(base_cmd, kwargs)
-    subprocess.run(cmd, check=True)  # nosec B603
+    if use_exec:
+        # make sure to flush stdout and stderr before replacing the process
+        sys.stdout.flush()
+        sys.stderr.flush()
+        os.execvpe(cmd[0], cmd, os.environ)  # nosec B606
+    else:
+        subprocess.run(cmd, check=True)  # nosec B603
 
 
 def _launch_torchrun_training(
-    cfg_file: str, kwargs: dict, launcher_args: list[str] | None = None
+    cfg_file: str,
+    kwargs: dict,
+    launcher_args: list[str] | None = None,
+    use_exec: bool = False,
 ) -> None:
     """Execute training via torchrun launcher."""
     launcher_args = launcher_args or []
@@ -178,7 +210,13 @@ def _launch_torchrun_training(
         base_cmd.append(cfg_file)
 
     cmd = build_command(base_cmd, kwargs)
-    subprocess.run(cmd, check=True)  # nosec B603
+    if use_exec:
+        # make sure to flush stdout and stderr before replacing the process
+        sys.stdout.flush()
+        sys.stderr.flush()
+        os.execvpe(cmd[0], cmd, os.environ)  # nosec B606
+    else:
+        subprocess.run(cmd, check=True)  # nosec B603
 
 
 def _launch_python_training(cfg_file: str, kwargs: dict) -> None:

src/axolotl/common/datasets.py

@@ -55,13 +55,11 @@ def load_datasets(
     """
     tokenizer = load_tokenizer(cfg)
     processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
-    preprocess_iterable = getattr(cli_args, "iterable", False)
 
     train_dataset, eval_dataset, total_num_steps, prompters = prepare_datasets(
         cfg,
         tokenizer,
         processor=processor,
-        preprocess_iterable=preprocess_iterable,
     )
 
     if (

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/datasets.py

@@ -1,18 +1,19 @@
-"""Module containing Dataset functionality"""
+"""
+Module containing dataset functionality.
+
+We want this to be a wrapper for an existing dataset that we have loaded. Lets use the
+concept of middlewares to wrap each dataset. We'll use the collators later on to pad the
+datasets.
+"""
+
+from typing import Any
 
-import torch
 from datasets import Dataset, IterableDataset
 
 from axolotl.utils.logging import get_logger
 
 from .prompt_tokenizers import PromptTokenizingStrategy
 
-# We want this to be a wrapper for an existing dataset that we have loaded
-# lets use the concept of middlewares to wrap each dataset, for example
-# ConstantLengthDataset(ShuffledDataset([TokenizedPromptDataset(alpaca_dataset)]))
-# let's check to ensure we don't truncate an item in the middle, we'll use
-# the collators later on to pad the datasets
-
 LOG = get_logger(__name__)
 
 
@@ -42,10 +43,13 @@ class TokenizedPromptDataset(Dataset):
             **kwargs,
         )
 
-    def process(self, dataset):
+    def process(self, dataset: Dataset | IterableDataset) -> Dataset | IterableDataset:
-        features = dataset.features.keys()
+        """Apply filtering and tokenization."""
+        features = None
+        if not isinstance(dataset, IterableDataset):
+            features = dataset.features.keys()
 
-        map_kwargs = {}
+        map_kwargs: dict[str, Any] = {}
         if self.prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
             map_kwargs["batch_size"] = 1_000
@@ -54,18 +58,28 @@ class TokenizedPromptDataset(Dataset):
             hasattr(self.prompt_tokenizer, "filter_rows")
             and self.prompt_tokenizer.filter_rows
         ):
+            filter_kwargs: dict[str, Any] = {"desc": "Strategy Filtering Rows"}
+            if not isinstance(dataset, IterableDataset):
+                filter_kwargs["num_proc"] = self.process_count
+
             dataset = dataset.filter(
                 self.prompt_tokenizer.filter_rows,
-                num_proc=self.process_count,
+                **filter_kwargs,
-                desc="Strategy Filtering Rows",
             )
 
+        map_kwargs = {
+            **map_kwargs,
+            "desc": "Tokenizing Prompts",
+        }
+
+        # Only add remove_columns for regular datasets
+        if not isinstance(dataset, IterableDataset):
+            map_kwargs["remove_columns"] = features
+            map_kwargs["num_proc"] = self.process_count
+            map_kwargs["keep_in_memory"] = self.keep_in_memory
+
         return dataset.map(
             self.prompt_tokenizer.tokenize_prompt,
-            num_proc=self.process_count,
-            remove_columns=features,
-            keep_in_memory=self.keep_in_memory,
-            desc="Tokenizing Prompts",
             **map_kwargs,
         )
 
@@ -79,140 +93,16 @@ def wrap_dataset_for_tokenized_prompt(
         map_kwargs = {}
         if prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
-        features = list(dataset.features.keys())
+
+        # Map the dataset and remove original columns
+        # For IterableDataset, features might be None until first iteration
+        remove_columns = None
+        if dataset.features is not None:
+            remove_columns = list(dataset.features.keys())
+
         return dataset.map(
             prompt_tokenizer.tokenize_prompt,
-            remove_columns=features,
+            remove_columns=remove_columns,
             **map_kwargs,
         )
     return TokenizedPromptDataset(prompt_tokenizer, dataset, **kwargs)
-
-
-# TODO this isn't the best since it can't interleave datasets
-class ConstantLengthDataset(IterableDataset):
-    """Iterable dataset that returns constant length chunks of tokens from stream of
-    text files.
-
-    Args:
-        tokenizer: The processor used for processing the data.
-        dataset: Dataset with text files.
-        seq_length: Length of token sequences to return.
-    """
-
-    def __init__(  # pylint: disable=super-init-not-called
-        self,
-        tokenizer,
-        datasets,
-        seq_length=2048,
-    ):
-        self.tokenizer = tokenizer
-        self.concat_token_id = tokenizer.eos_token_id
-        self.datasets: list[IterableDataset] = datasets
-        self.seq_length = seq_length
-
-        vocab_size = len(tokenizer.get_vocab())
-
-        if vocab_size <= torch.iinfo(torch.int16).max:
-            self.tokens_dtype = torch.int16
-        elif vocab_size <= torch.iinfo(torch.int32).max:
-            self.tokens_dtype = torch.int32
-        else:
-            self.tokens_dtype = torch.int64
-
-    def __iter__(self):
-        buffer = {
-            "input_ids": [],
-            "attention_mask": [],
-            "labels": [],
-            "position_ids": [],
-        }
-        buffer_len = 0
-        for dataset in self.datasets:
-            idx = 0
-            iterator = iter(dataset)
-            more_examples = True
-            while more_examples:
-                try:
-                    example = next(iterator)
-                    idx += 1
-                except StopIteration:
-                    more_examples = False
-                    example = None
-
-                add_concat_token = False
-                if example:
-                    example_len = len(example["input_ids"])
-                    add_concat_token = example["input_ids"][-1] != self.concat_token_id
-                else:
-                    example_len = 0
-
-                if not example_len or (
-                    buffer_len + int(add_concat_token) + example_len > self.seq_length
-                ):
-                    if buffer["input_ids"]:
-                        input_ids = torch.cat(buffer["input_ids"], dim=-1)[
-                            : self.seq_length
-                        ]
-                        attention_mask = torch.cat(buffer["attention_mask"], dim=-1)[
-                            : self.seq_length
-                        ]
-                        position_ids = torch.cat(buffer["position_ids"], dim=-1)[
-                            : self.seq_length
-                        ]
-                        labels = torch.cat(buffer["labels"], dim=-1)[: self.seq_length]
-                        if labels.size() == input_ids.size() and (
-                            attention_mask.size() == input_ids.size()
-                        ):
-                            yield {
-                                "input_ids": input_ids,
-                                "labels": labels,
-                                "attention_mask": attention_mask,
-                                "position_ids": position_ids,
-                            }
-                        else:
-                            LOG.warning(
-                                "Dropping batch due to tensor size mismatch "
-                                f"input_ids: {input_ids.size()}, "
-                                f"labels: {labels.size()}, "
-                                f"attention_mask: {attention_mask.size()}"
-                            )
-                    buffer = {
-                        "input_ids": [],
-                        "attention_mask": [],
-                        "labels": [],
-                        "position_ids": [],
-                    }
-                    buffer_len = 0
-                    idx = 1
-
-                if example:
-                    # FIXME
-                    # just going to drop data points that are too long
-                    if len(example["input_ids"]) <= self.seq_length:
-                        input_ids = example["input_ids"]
-                        attention_mask = example["attention_mask"]
-                        labels = example["labels"]
-
-                        if add_concat_token:
-                            input_ids.append(self.concat_token_id)
-                            attention_mask.append(1)
-                            labels.append(self.concat_token_id)
-
-                        input_ids_with_concat = torch.tensor(
-                            input_ids, dtype=self.tokens_dtype
-                        )
-                        attention_mask_with_concat = torch.tensor(
-                            [idx * m for m in attention_mask], dtype=torch.int16
-                        )
-                        labels_with_concat = torch.tensor(
-                            labels, dtype=self.tokens_dtype
-                        )
-                        position_ids = torch.arange(
-                            len(input_ids), dtype=self.tokens_dtype
-                        )
-
-                        buffer["input_ids"].append(input_ids_with_concat)
-                        buffer["attention_mask"].append(attention_mask_with_concat)
-                        buffer["labels"].append(labels_with_concat)
-                        buffer["position_ids"].append(position_ids)
-                        buffer_len += len(input_ids)

src/axolotl/integrations/base.py

@@ -76,8 +76,8 @@ class BasePlugin:
     def __init__(self):
         """Initializes the BasePlugin."""
 
-    def register(self, cfg: DictDefault):  # pylint: disable=unused-argument
+    def register(self, cfg: dict):  # pylint: disable=unused-argument
-        """Registers the plugin with the given configuration.
+        """Registers the plugin with the given configuration as an unparsed dict.
 
         Args:
             cfg: The configuration for the plugin.

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

@@ -73,9 +73,6 @@ class PatchManager:
         self._apply_voxtral_patches()
 
     def _apply_transformers_patches(self):
-        from axolotl.monkeypatch.transformers.modeling_flash_attention_utils import (
-            patch_prepare_from_posids,
-        )
         from axolotl.monkeypatch.transformers.trainer_loss_calc import (
             patch_evaluation_loop,
             patch_maybe_log_save_evaluate,
@@ -87,7 +84,6 @@ class PatchManager:
             and self.cfg.fsdp_version == 2
         )
 
-        patch_prepare_from_posids()
         patch_evaluation_loop(patch_fsdp2)
         patch_maybe_log_save_evaluate()
 
@@ -289,12 +285,10 @@ 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/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/monkeypatch/transformers/modeling_flash_attention_utils.py

@@ -1,87 +0,0 @@
-"""
-Monkey patch to fix transformers.modeling_flash_attention_utils.
-
-see https://github.com/huggingface/transformers/pull/39653/files
-"""
-
-import sys
-
-import torch
-
-
-def _prepare_from_posids(query, key, value, position_ids):
-    """
-    This function returns necessary arguments to call `flash_attn_varlen_func`.
-    All three query, key, value states will be flattened.
-    Cumulative lengths of each examples in the batch will be extracted from position_ids.
-    NOTE: ideally cumulative lengths should be prepared at the data collator stage
-    Arguments:
-        query (`torch.Tensor`):
-            Query state with padding. Shape: (batch_size, query_length, num_heads, head_dim).
-        key (`torch.Tensor`):
-            Key state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
-        value (`torch.Tensor`):
-            Value state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
-        position_ids (`torch.Tensor`):
-            Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
-    Return:
-        query (`torch.Tensor`):
-            Query state without padding. Shape: (total_target_length, num_heads, head_dim).
-        key (`torch.Tensor`):
-            Key state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
-        value (`torch.Tensor`):
-            Value state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
-        indices_q (`torch.Tensor`):
-            The indices of non-masked tokens from the flattened input target sequence.
-        (cu_seqlens_q, cu_seqlens_k) (`tuple[int]`):
-            The cumulative sequence lengths for the target (query) and source (key, value), used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
-        (max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`tuple[int]`):
-            Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence i.e. query, `max_seqlen_in_batch_k` for the source sequence i.e. key/value).
-    """
-    query = query.contiguous().view(-1, query.size(-2), query.size(-1))
-    key = key.contiguous().view(-1, key.size(-2), key.size(-1))
-    value = value.contiguous().view(-1, value.size(-2), value.size(-1))
-
-    position_ids = position_ids.flatten()
-    indices_q = torch.arange(
-        position_ids.size(0), device=position_ids.device, dtype=torch.int32
-    )
-
-    cu_seq_lens = torch.cat(
-        (
-            indices_q[position_ids == 0],
-            torch.tensor(
-                position_ids.size(), device=position_ids.device, dtype=torch.int32
-            ),
-        )
-    )
-    # NOTE: With torch compile, this will cause a graph break if you don't set
-    # `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1` in the environment or call
-    # `torch._dynamo.config.capture_scalar_outputs = True` before doing the forward pass.
-    # This is a limitation of flash attention API, as the function `flash_attn_varlen_func`
-    # requires `max_length_q`, `max_length_k` to be passed as `int` and not `torch.Tensor`.
-    # https://github.com/Dao-AILab/flash-attention/blob/2dd8078adc1d9b74e315ee99718c0dea0de8eeb6/flash_attn/flash_attn_interface.py#L1423-L1424
-    # We should use cu_seq_lens instead of position_ids to get the max length since position_ids is not always increasing
-    # for some models (e.g. qwen2-vl).
-    max_length = cu_seq_lens.diff().max().item()
-    return (
-        query,
-        key,
-        value,
-        indices_q,
-        (cu_seq_lens, cu_seq_lens),
-        (max_length, max_length),
-    )
-
-
-def patch_prepare_from_posids():
-    import transformers.modeling_flash_attention_utils
-
-    transformers.modeling_flash_attention_utils._prepare_from_posids = (  # pylint: disable=protected-access
-        _prepare_from_posids
-    )
-    setattr(
-        sys.modules["transformers.modeling_flash_attention_utils"],
-        "_prepare_from_posids",
-        _prepare_from_posids,
-    )

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

@@ -9,6 +9,7 @@ from datasets import (
     Dataset,
     DatasetDict,
     IterableDataset,
+    IterableDatasetDict,
     load_dataset,
 )
 from transformers import PreTrainedTokenizer, ProcessorMixin
@@ -28,7 +29,7 @@ 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
@@ -43,12 +44,24 @@ from axolotl.utils.trainer import (
 LOG = get_logger(__name__)
 
 
+def _is_streaming_enabled(cfg: DictDefault) -> bool:
+    """Check if streaming is enabled for a specific split."""
+    streaming = cfg.get("streaming")
+    if streaming is True:
+        return True
+
+    # Check if pretraining dataset exists (defaults to streaming)
+    has_pretraining = cfg.get("pretraining_dataset") is not None
+    streaming = has_pretraining and streaming is None
+
+    return streaming
+
+
 @retry_on_request_exceptions(max_retries=3, delay=5)
 def prepare_datasets(
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizer,
     processor: ProcessorMixin | None = None,
-    preprocess_iterable: bool = False,
 ) -> tuple[IterableDataset | Dataset, Dataset | None, int, list[Prompter | None]]:
     """Prepare training and evaluation datasets based on configuration.
 
@@ -56,23 +69,19 @@ def prepare_datasets(
         cfg: Dictionary mapping `axolotl` config keys to values.
         tokenizer: Tokenizer to use for processing text.
         processor: Optional processor for multimodal datasets.
-        preprocess_iterable: Whether to use iterable preprocessing.
 
     Returns:
         Tuple of (train_dataset, eval_dataset, total_steps, prompters).
     """
     if cfg.pretraining_dataset:
-        return _prepare_pretraining_dataset(
+        return _prepare_pretraining_dataset(cfg, tokenizer, processor)
-            cfg, tokenizer, processor, preprocess_iterable
+    return _prepare_standard_dataset(cfg, tokenizer, processor)
-        )
-    return _prepare_standard_dataset(cfg, tokenizer, processor, preprocess_iterable)
 
 
 def _prepare_standard_dataset(
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizer,
     processor: ProcessorMixin | None,
-    preprocess_iterable: bool,
 ) -> tuple[Dataset, Dataset | None, int, list[Prompter | None]]:
     """Prepare standard (non-pretraining) datasets."""
 
@@ -83,7 +92,6 @@ def _prepare_standard_dataset(
             cfg,
             split="train",
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
 
         # Overwrite eval_dataset if test data exists
@@ -93,7 +101,6 @@ def _prepare_standard_dataset(
                 cfg,
                 split="test",
                 processor=processor,
-                preprocess_iterable=preprocess_iterable,
             )
 
         return train_dataset, eval_dataset, prompters
@@ -109,7 +116,12 @@ def _prepare_standard_dataset(
         return train_dataset, eval_dataset, -1, prompters
 
     # Validate sample packing configuration for evaluation
-    if eval_dataset and cfg.sample_packing and cfg.eval_sample_packing is not False:
+    if (
+        eval_dataset
+        and cfg.sample_packing
+        and cfg.eval_sample_packing is not False
+        and not isinstance(eval_dataset, IterableDataset)
+    ):
         total_eval_steps = calculate_total_num_steps(cfg, eval_dataset, update=False)
         if total_eval_steps == 0:
             raise ValueError(
@@ -117,13 +129,17 @@ def _prepare_standard_dataset(
                 "You should set `eval_sample_packing: False` in your config."
             )
 
-    # Calculate total number of training steps
+    # Set total_num_steps for training
-    if cfg.max_steps:
+    if isinstance(train_dataset, IterableDataset):
-        total_num_steps = min(
+        total_num_steps = cfg.max_steps
-            calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
-        )
     else:
-        total_num_steps = calculate_total_num_steps(cfg, train_dataset)
+        if cfg.max_steps:
+            total_num_steps = min(
+                calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
+            )
+        else:
+            total_num_steps = calculate_total_num_steps(cfg, train_dataset)
+
     LOG.info(f"Maximum number of steps set at {total_num_steps}")
     return train_dataset, eval_dataset, total_num_steps, prompters
 
@@ -132,7 +148,6 @@ def _prepare_pretraining_dataset(
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizer,
     processor: ProcessorMixin | None,
-    preprocess_iterable: bool,
 ) -> tuple[IterableDataset, Dataset | None, int, list[Prompter | None]]:
     """
     Prepare dataset for pretraining mode.
@@ -153,7 +168,6 @@ def _prepare_pretraining_dataset(
             cfg,
             split="test",
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
 
     if cfg.dataset_exact_deduplication:
@@ -256,7 +270,6 @@ def _load_tokenized_prepared_datasets(
     cfg: DictDefault,
     split: Literal["train", "test"] = "train",
     processor: ProcessorMixin | None = None,
-    preprocess_iterable: bool = False,
 ) -> tuple[Dataset | DatasetDict, list[Prompter | None]]:
     """Load or create tokenized and prepared datasets for training or testing.
 
@@ -265,39 +278,51 @@ def _load_tokenized_prepared_datasets(
         cfg: Configuration object.
         split: Dataset split to load ('train' or 'test').
         processor: Optional processor for multimodal datasets.
-        preprocess_iterable: Whether to use iterable preprocessing.
 
     Returns:
         Tuple of (dataset, prompters list).
     """
-    # Select correct dataset configuration based on split
     datasets_configs = cfg.datasets if split == "train" else cfg.test_datasets
-
-    # Generate dataset hash for caching
-    dataset_hash = generate_dataset_hash_from_config(
-        cfg, datasets_configs, tokenizer.name_or_path
-    )
-
-    # Try loading from hub if push_dataset_to_hub is configured
-    dataset = None
-    if cfg.push_dataset_to_hub:
-        dataset = try_load_from_hub(cfg, dataset_hash, split)
-
-    # If not found on hub, try loading from disk
-    if dataset is None:
-        dataset = load_preprocessed_dataset(cfg, dataset_hash)
-
-    # If not found on disk or skipping prepared dataset, load and process raw datasets
     prompters: list[Prompter | None] = []
-    if dataset is None:
+
+    use_streaming = False
+    if split == "train":
+        use_streaming = _is_streaming_enabled(cfg)
+
+    if use_streaming:
+        # For streaming datasets, skip caching and load raw datasets directly
         dataset, prompters = _load_raw_datasets(
             cfg,
             datasets_configs,
             tokenizer,
             split,
             processor,
-            preprocess_iterable,
         )
+    else:
+        # Generate dataset hash for caching
+        dataset_hash = generate_dataset_hash_from_config(
+            cfg, datasets_configs, tokenizer.name_or_path
+        )
+
+        # Try loading from hub if push_dataset_to_hub is configured
+        dataset = None
+        if cfg.push_dataset_to_hub:
+            dataset = try_load_from_hub(cfg, dataset_hash, split)
+
+        # If not found on hub, try loading from disk
+        if dataset is None:
+            dataset = load_preprocessed_dataset(cfg, dataset_hash)
+
+        # If not found on disk or skipping prepared dataset, load and process raw
+        # datasets
+        if dataset is None:
+            dataset, prompters = _load_raw_datasets(
+                cfg,
+                datasets_configs,
+                tokenizer,
+                split,
+                processor,
+            )
 
     return dataset, prompters
 
@@ -306,9 +331,8 @@ def _load_raw_datasets(
     cfg: DictDefault,
     datasets_configs: list,
     tokenizer: PreTrainedTokenizer,
-    split: str,
+    split: Literal["train", "test"],
     processor: ProcessorMixin | None = None,
-    preprocess_iterable: bool = False,
 ) -> tuple[Dataset, list[Prompter | None]]:
     """Load, process, merge, and save raw datasets."""
     LOG.info("Loading raw datasets...", main_process_only=False)
@@ -329,7 +353,6 @@ def _load_raw_datasets(
             split=split,
             seed=cfg.seed,
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
         datasets.append(dataset_wrapper)
         prompters.append(dataset_prompter)
@@ -339,17 +362,18 @@ 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)
 
-        # Save the prepared dataset
+        # Only save regular datasets to disk, not streaming datasets
-        dataset_hash = generate_dataset_hash_from_config(
+        if not isinstance(dataset, IterableDataset):
-            cfg, datasets_configs, tokenizer.name_or_path
+            dataset_hash = generate_dataset_hash_from_config(
-        )
+                cfg, datasets_configs, tokenizer.name_or_path
-        save_preprocessed_dataset(cfg, dataset, dataset_hash, split)
+            )
+            save_preprocessed_dataset(cfg, dataset, dataset_hash, split)
 
     return dataset, prompters
 
@@ -358,22 +382,22 @@ def _load_and_process_single_dataset(
     dataset_config: DictDefault,
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizer,
-    split: str,
+    split: Literal["train", "test"],
     seed: int,
     processor: ProcessorMixin | None = None,
-    preprocess_iterable: bool = False,
 ) -> tuple[Dataset | IterableDataset, Prompter | None]:
     """Load and process a single dataset based on the passed config."""
-    # Load the dataset
+    use_streaming = False
-    dataset = load_dataset_with_config(
+    if split == "train":
-        dataset_config, cfg.hf_use_auth_token, streaming=preprocess_iterable
+        use_streaming = _is_streaming_enabled(cfg)
-    )
 
-    # Parse dataset type
+    dataset = load_dataset_with_config(
+        dataset_config, cfg.hf_use_auth_token, use_streaming
+    )
     d_base_type, d_prompt_style = _parse_dataset_type(dataset_config.type)
 
     # Select the appropriate split
-    if isinstance(dataset, DatasetDict):
+    if isinstance(dataset, (DatasetDict, IterableDatasetDict)):
         if dataset_config.split and dataset_config.split in dataset:
             dataset = dataset[dataset_config.split]
         elif split in dataset:
@@ -418,11 +442,13 @@ def _parse_dataset_type(d_type: str) -> tuple[str | None, str | None]:
 
 
 def _handle_train_dataset_split(
-    dataset: Dataset, cfg: DictDefault
+    dataset: Dataset | IterableDataset, cfg: DictDefault
-) -> tuple[Dataset, Dataset | None]:
+) -> tuple[Dataset | IterableDataset, Dataset | IterableDataset | None]:
     """Handle processing for train split, including validation set creation."""
     val_set_size = (
-        int(cfg.val_set_size) if cfg.val_set_size > 1 else float(cfg.val_set_size)
+        int(cfg.val_set_size)
+        if cfg.val_set_size and cfg.val_set_size > 1
+        else float(cfg.val_set_size or 0.0)
     )
 
     if val_set_size:
@@ -433,27 +459,33 @@ def _handle_train_dataset_split(
         return train_dataset, eval_dataset
 
     # No validation split - apply deduplication if needed and return as train dataset
-    if cfg.dataset_exact_deduplication:
+    if cfg.dataset_exact_deduplication and not isinstance(dataset, IterableDataset):
         train_dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
     else:
+        if cfg.dataset_exact_deduplication and isinstance(dataset, IterableDataset):
+            LOG.info("Deduplication skipped for streaming datasets (not compatible)")
         train_dataset = dataset
 
     return train_dataset, None
 
 
 def _handle_test_dataset_split(
-    dataset: Dataset, cfg: DictDefault
+    dataset: Dataset | IterableDataset, cfg: DictDefault
-) -> tuple[None, Dataset | None]:
+) -> tuple[None, Dataset | IterableDataset | None]:
     """Handle processing for test split."""
-    if cfg.dataset_exact_deduplication:
+    if cfg.dataset_exact_deduplication and not isinstance(dataset, IterableDataset):
         eval_dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
     else:
+        if cfg.dataset_exact_deduplication and isinstance(dataset, IterableDataset):
+            LOG.info("Deduplication skipped for streaming datasets (not compatible)")
         eval_dataset = dataset
 
     return None, eval_dataset
 
 
-def _apply_dataset_sharding(dataset: Dataset, cfg: DictDefault) -> Dataset:
+def _apply_dataset_sharding(
+    dataset: Dataset | IterableDataset, cfg: DictDefault
+) -> Dataset | IterableDataset:
     """Apply dataset sharding if configured.
 
     Args:
@@ -479,7 +511,6 @@ def _load_and_prepare_datasets(
     cfg: DictDefault,
     split: Literal["train", "test"] = "train",
     processor: ProcessorMixin | None = None,
-    preprocess_iterable: bool = False,
 ) -> tuple[Dataset | None, Dataset | None, list[Prompter | None]]:
     """Load and prepare datasets with optional validation split and sharding.
 
@@ -488,7 +519,6 @@ def _load_and_prepare_datasets(
         cfg: Configuration object.
         split: Dataset split to load ('train' or 'test').
         processor: Optional processor for multimodal datasets.
-        preprocess_iterable: Whether to use iterable preprocessing.
 
     Returns:
         Tuple of (train_dataset, eval_dataset, prompters).
@@ -499,7 +529,6 @@ def _load_and_prepare_datasets(
         cfg,
         split=split,
         processor=processor,
-        preprocess_iterable=preprocess_iterable,
     )
 
     # Apply dataset sharding if configured using shared function

src/axolotl/utils/data/shared.py

@@ -13,6 +13,7 @@ from datasets import (
     IterableDataset,
     IterableDatasetDict,
     concatenate_datasets,
+    interleave_datasets,
     load_dataset,
     load_from_disk,
 )
@@ -524,7 +525,9 @@ def generate_dataset_hash_from_config(
     return str(md5(config_str))
 
 
-def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
+def merge_datasets(
+    datasets: list[Dataset | IterableDataset], cfg: DictDefault
+) -> Dataset | IterableDataset:
     """Merge multiple datasets into one with optional shuffling.
 
     Args:
@@ -537,23 +540,23 @@ def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
     if len(datasets) == 1:
         ds = datasets[0]
 
-        # Do not shuffle if curriculum sampling is enabled or
+        if (
-        # shuffle_merged_datasets is disabled
+            cfg.curriculum_sampling
-        if cfg.curriculum_sampling or not cfg.shuffle_merged_datasets:
+            or not cfg.shuffle_merged_datasets
+            or isinstance(ds, IterableDataset)
+        ):
             return ds
-
         return ds.shuffle(seed=cfg.seed)
 
-    # If enabled, shuffle each dataset independently before merging.
+    if cfg.shuffle_before_merging_datasets and all(
-    # This allows curriculum learning strategies to be applied at the dataset level.
+        isinstance(ds, Dataset) for ds in datasets
-    if cfg.shuffle_before_merging_datasets:
+    ):
         LOG.info("Shuffling each dataset individually before merging...")
         datasets = [ds.shuffle(seed=cfg.seed) for ds in datasets]
 
-    LOG.info("Merging datasets...")
+    merged_dataset = _merge_datasets_with_strategy(datasets, cfg)
-    merged_dataset = concatenate_datasets(datasets)
 
-    if cfg.shuffle_merged_datasets:
+    if cfg.shuffle_merged_datasets and not isinstance(merged_dataset, IterableDataset):
         LOG.debug("Shuffling merged datasets...")
         if cfg.curriculum_sampling:
             LOG.warning(
@@ -562,6 +565,45 @@ def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
             )
         merged_dataset = merged_dataset.shuffle(seed=cfg.seed)
     else:
-        LOG.debug("Not shuffling merged datasets.")
+        if isinstance(merged_dataset, IterableDataset):
+            LOG.debug("Skipping shuffle for streaming datasets.")
+        else:
+            LOG.debug("Not shuffling merged datasets.")
 
     return merged_dataset
+
+
+def _merge_datasets_with_strategy(
+    datasets: list[Dataset | IterableDataset], cfg: DictDefault
+) -> Dataset | IterableDataset:
+    """
+    Merge datasets using the configured mixing strategy. Works with streaming and non-
+    streaming datasets.
+
+    Args:
+        datasets: List of datasets to merge.
+        cfg: Configuration object containing mixing settings.
+
+    Returns:
+        Merged dataset (Dataset or IterableDataset depending on inputs).
+    """
+    strategy = cfg.get("dataset_mixing_strategy", "concatenate")
+    weights = cfg.get("mixing_weights", None)
+
+    LOG.info(f"Merging datasets with mixing strategy: {strategy}...")
+
+    if strategy == "concatenate":
+        if not all(isinstance(ds, Dataset) for ds in datasets):
+            raise ValueError(
+                "Cannot concatenate streaming datasets. Use 'round_robin', 'weighted', "
+                "or 'random' instead."
+            )
+        return concatenate_datasets(datasets)
+    if strategy == "round_robin":
+        return interleave_datasets(datasets, seed=cfg.seed)
+    if strategy == "weighted":
+        return interleave_datasets(datasets, probabilities=weights, seed=cfg.seed)
+    if strategy == "random":
+        equal_weights = [1.0 / len(datasets)] * len(datasets)
+        return interleave_datasets(datasets, probabilities=equal_weights, seed=cfg.seed)
+    raise ValueError(f"Unknown dataset mixing strategy: {strategy}")

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.
@@ -161,11 +190,15 @@ def drop_long_seq_in_dataset(
     Returns:
         Filtered dataset with long sequences removed.
     """
-    if "input_ids" not in dataset.column_names:
+    if hasattr(dataset, "column_names") and dataset.column_names:
-        LOG.warning(
+        if "input_ids" not in dataset.column_names:
-            "Dataset does not contain 'input_ids' column. Skip drop long seq. This is "
+            LOG.warning(
-            "expected for reward modeling."
+                "Dataset does not contain 'input_ids' column. Skip drop long seq. This "
-        )
+                "is expected for reward modeling."
+            )
+            return dataset
+    elif isinstance(dataset, IterableDataset):
+        LOG.info("Skipping drop_long_seq for streaming datasets (not compatible)")
         return dataset
 
     drop_long = functools.partial(
@@ -192,8 +225,21 @@ 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 +247,11 @@ 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={
@@ -926,9 +932,27 @@ class AxolotlInputConfig(
 
     fix_untrained_tokens: int | list[int] | None = None
 
+    streaming: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Whether to use streaming datasets (IterableDataset) for training datasets. When True, data is loaded on-demand during training without upfront preprocessing. Requires max_steps to be set. Pre-training datasets default to streaming unless explicitly set to False."
+        },
+    )
+    dataset_mixing_strategy: str | None = Field(
+        default="round_robin",
+        json_schema_extra={
+            "description": "Strategy for mixing multiple datasets: 'concatenate', 'round_robin' (equal sampling), 'weighted' (use mixing_weights), or 'random' (random sampling with equal probability). Works for both streaming and non-streaming datasets."
+        },
+    )
+    mixing_weights: list[float] | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Weights for weighted mixing strategy when using multiple datasets. Must sum to 1.0 and have same length as datasets list. Only used when dataset_mixing_strategy='weighted'."
+        },
+    )
+
     # INTERNALS - document for now, generally not set externally
     is_preprocess: bool | None = None
-    preprocess_iterable: bool | None = None
 
     total_num_tokens: int | None = Field(
         default=None,

src/axolotl/utils/schemas/training.py

@@ -161,7 +161,12 @@ class HyperparametersConfig(BaseModel):
     max_grad_norm: float | None = Field(
         default=None, json_schema_extra={"description": "Gradient clipping max norm"}
     )
-    num_epochs: float = Field(default=1.0)
+    num_epochs: float = Field(
+        default=1.0,
+        json_schema_extra={
+            "description": "Number of iterations over dataset for training"
+        },
+    )
 
     @field_validator("batch_size")
     @classmethod

src/axolotl/utils/schemas/validation.py

@@ -3,6 +3,8 @@
 # pylint: disable=too-many-boolean-expressions
 
 import json
+import os
+import sys
 import tempfile
 from pathlib import Path
 
@@ -191,6 +193,7 @@ class AttentionValidationMixin:
         return data
 
 
+# pylint: disable=too-many-public-methods
 class TrainingValidationMixin:
     """Validation methods related to training configuration."""
 
@@ -369,10 +372,10 @@ 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 "
@@ -507,11 +510,58 @@ class TrainingValidationMixin:
             # combining these would raise `TypeError: cannot pickle 'dict_keys' object`
             # due to trying to count the number of tokens total in the dataset
             raise ValueError(
-                "pretraining_dataset and include_tokens_per_second cannot be used together."
+                "pretraining_dataset and include_tokens_per_second cannot be used "
+                "together."
             )
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_max_steps_num_epochs_conflict(cls, data):
+        """Handle max_steps and num_epochs configuration and auto-set defaults."""
+        max_steps = data.get("max_steps")
+        num_epochs = data.get("num_epochs")
+
+        # Auto-set num_epochs to 1 if neither max_steps nor num_epochs are set
+        if max_steps is None and num_epochs is None:
+            data["num_epochs"] = 1.0
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_saves_per_epoch_conflicts(cls, data):
+        """Ensure saves_per_epoch is compatible with training configuration."""
+        saves_per_epoch = data.get("saves_per_epoch")
+        num_epochs = data.get("num_epochs")
+
+        if saves_per_epoch is not None:
+            # Check if saves_per_epoch is set but num_epochs is unset
+            if num_epochs is None:
+                raise ValueError(
+                    "saves_per_epoch requires num_epochs to be set to calculate save "
+                    "intervals."
+                )
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_evals_per_epoch_conflicts(cls, data):
+        """Ensure evals_per_epoch is compatible with training configuration."""
+        evals_per_epoch = data.get("evals_per_epoch")
+        num_epochs = data.get("num_epochs")
+
+        if evals_per_epoch is not None:
+            if num_epochs is None:
+                raise ValueError(
+                    "evals_per_epoch requires num_epochs to be set to calculate "
+                    "evaluation intervals."
+                )
+
+        return data
+
 
 class LoRAValidationMixin:
     """Validation methods related to LoRA/QLoRA configuration."""
@@ -817,13 +867,13 @@ 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")
@@ -1077,6 +1127,27 @@ class PretrainingValidationMixin:
                     data["accelerator_config"]["dispatch_batches"] = False
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_streaming_split_batches_accelerate(cls, data):
+        # Check if streaming is enabled for training
+        streaming = data.get("streaming", False)
+
+        # If streaming is enabled, configure accelerator
+        if streaming:
+            accelerator_config = data.get("accelerator_config", {})
+            if not accelerator_config:
+                data["accelerator_config"] = {
+                    "split_batches": False,
+                    "dispatch_batches": False,
+                }
+            else:
+                if accelerator_config.get("split_batches") is None:
+                    data["accelerator_config"]["split_batches"] = False
+                if accelerator_config.get("dispatch_batches") is None:
+                    data["accelerator_config"]["dispatch_batches"] = False
+        return data
+
 
 class ModelCompatibilityValidationMixin:
     """Validation methods for specific model compatibility."""
@@ -1151,10 +1222,8 @@ 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 +1320,26 @@ 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:
@@ -1321,6 +1406,128 @@ class GRPOVllmValidationMixin:
         return self
 
 
+class StreamingValidationMixin:
+    """Validation methods related to streaming datasets."""
+
+    def _is_streaming_enabled(self) -> bool:
+        """Check if streaming is enabled."""
+        # Fall back to main streaming setting
+        streaming = getattr(self, "streaming", None)
+        if streaming is True:
+            return True
+
+        # Check if pretraining dataset exists (defaults to streaming)
+        has_pretraining = getattr(self, "pretraining_dataset", None) is not None
+        streaming = has_pretraining and streaming is None
+
+        return streaming
+
+    @model_validator(mode="after")
+    def check_streaming_requires_max_steps(self):
+        """Ensure max_steps is set when using streaming datasets."""
+        # Check if streaming is enabled for training datasets
+        if self._is_streaming_enabled():
+            max_steps = getattr(self, "max_steps", None)
+            if not max_steps:
+                raise ValueError("max_steps must be set when using streaming datasets")
+
+        return self
+
+    @model_validator(mode="after")
+    def check_streaming_validation_splits_conflict(self):
+        """Ensure validation splits are not used with streaming datasets."""
+        # Check if streaming is enabled for training datasets
+        if self._is_streaming_enabled():
+            val_set_size = getattr(self, "val_set_size", 0.0)
+            if val_set_size and val_set_size > 0:
+                raise ValueError(
+                    "Validation splits not supported for streaming datasets, please "
+                    "use test_datasets: ... instead"
+                )
+
+        return self
+
+    @model_validator(mode="after")
+    def check_streaming_preprocessing_conflict(self):
+        """Ensure preprocessing is not enabled with streaming datasets."""
+        # Check if streaming is enabled for training datasets
+        if self._is_streaming_enabled():
+            if os.environ.get("AXOLOTL_IS_PREPROCESS") == "1":
+                raise ValueError("preprocess is not supported for streaming datasets")
+
+        return self
+
+    @model_validator(mode="after")
+    def check_dataset_mixing_weights(self):
+        """Validate dataset mixing weights configuration."""
+        valid_strategies = ["concatenate", "round_robin", "weighted", "random"]
+
+        # Get datasets to validate length against
+        datasets = getattr(self, "datasets", None)
+
+        # Check main strategy and weights
+        strategy = getattr(self, "dataset_mixing_strategy", "concatenate")
+        weights = getattr(self, "mixing_weights", None)
+
+        dataset_count = len(datasets) if datasets else 0
+        self._validate_dataset_strategy_and_weights(
+            strategy,
+            weights,
+            "dataset_mixing_strategy",
+            "mixing_weights",
+            valid_strategies,
+            dataset_count,
+        )
+
+        return self
+
+    def _validate_dataset_strategy_and_weights(
+        self,
+        strategy,
+        weights,
+        strategy_field,
+        weights_field,
+        valid_strategies,
+        dataset_count,
+    ):
+        """Helper method to validate dataset mixing strategy and weights pair."""
+        if strategy not in valid_strategies:
+            raise ValueError(
+                f"{strategy_field} must be one of {valid_strategies}, "
+                f"got '{strategy}'"
+            )
+
+        if strategy == "weighted":
+            if weights is None:
+                raise ValueError(
+                    f"{weights_field} must be provided when "
+                    f"{strategy_field}='weighted'"
+                )
+
+            if not isinstance(weights, list) or not all(
+                isinstance(w, (int, float)) for w in weights
+            ):
+                raise ValueError(f"{weights_field} must be a list of numbers")
+
+            if any(w < 0 for w in weights):
+                raise ValueError(f"{weights_field} must be non-negative")
+
+            if abs(sum(weights) - 1.0) > 1e-6:
+                raise ValueError(f"{weights_field} must sum to 1.0, got {sum(weights)}")
+
+            # Validate weights length against dataset count
+            if dataset_count > 0 and len(weights) != dataset_count:
+                raise ValueError(
+                    f"{weights_field} length ({len(weights)}) must match number of datasets ({dataset_count})"
+                )
+
+        elif weights is not None and strategy != "weighted":
+            LOG.warning(
+                f"{weights_field} provided but {strategy_field} is '{strategy}'. "
+                "Weights will be ignored."
+            )
+
+
 # pylint: disable=too-many-ancestors
 class ValidationMixin(
     DatasetValidationMixin,
@@ -1332,6 +1539,7 @@ class ValidationMixin(
     SystemValidationMixin,
     ChatTemplateValidationMixin,
     PretrainingValidationMixin,
+    StreamingValidationMixin,
     ModelCompatibilityValidationMixin,
     ComplexValidationMixin,
     GRPOVllmValidationMixin,

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

src/axolotl/utils/trainer.py

@@ -10,7 +10,6 @@ from typing import List, Optional
 
 import numpy as np
 import torch
-import torch.cuda
 from datasets import IterableDataset, disable_caching, enable_caching
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from transformers.utils import is_torch_bf16_gpu_available
@@ -23,6 +22,65 @@ from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 LOG = get_logger(__name__)
 
 
+def _create_filtered_iterable_dataset(dataset, filter_fn, batched=False):
+    """
+    Create a filtered IterableDataset that works around a HuggingFace datasets
+    limitation.
+    """
+
+    def filtered_generator():
+        """Generator that yields only samples that pass the filter function."""
+        if batched:
+            batch = []
+            batch_size = 1000  # Process in batches of 1000
+
+            for sample in dataset:
+                batch.append(sample)
+
+                if len(batch) >= batch_size:
+                    # Create a batch dict from list of samples
+                    batch_dict = {}
+                    for key in batch[0].keys():
+                        batch_dict[key] = [sample[key] for sample in batch]
+
+                    # Apply filter function to batch
+                    keep_mask = filter_fn(batch_dict)
+
+                    # Yield samples that should be kept
+                    for i, keep in enumerate(keep_mask):
+                        if keep:
+                            yield batch[i]
+
+                    batch = []
+
+            # Process remaining samples in batch
+            if batch:
+                batch_dict = {}
+                for key in batch[0].keys():
+                    batch_dict[key] = [sample[key] for sample in batch]
+
+                keep_mask = filter_fn(batch_dict)
+
+                for i, keep in enumerate(keep_mask):
+                    if keep:
+                        yield batch[i]
+        else:
+            # For non-batched filtering, apply filter to each sample individually
+            for sample in dataset:
+                if filter_fn(sample):
+                    yield sample
+
+    # Create new IterableDataset from the filtered generator
+    filtered_dataset = IterableDataset.from_generator(filtered_generator)
+
+    # Preserve the original features if they exist
+    # pylint:disable=protected-access
+    if hasattr(dataset, "_info") and dataset._info.features is not None:
+        filtered_dataset._info.features = dataset._info.features
+
+    return filtered_dataset
+
+
 @torch.jit.script
 def weighted_cross_entropy(
     logits: torch.Tensor, labels: torch.Tensor, weights: torch.Tensor
@@ -282,12 +340,21 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
     drop_long_kwargs = {}
     if filter_map_kwargs:
         drop_long_kwargs["desc"] = "Drop Samples with Zero Trainable Tokens"
-    train_dataset = train_dataset.filter(
+
-        drop_no_trainable_tokens,
+    # For IterableDatasets, always use custom filtering to avoid features issues
-        batched=True,
+    if isinstance(train_dataset, IterableDataset):
-        **filter_map_kwargs,
+        # IterableDatasets often have None features after transformations,
-        **drop_long_kwargs,
+        # so we use our custom filter implementation that doesn't rely on features
-    )
+        train_dataset = _create_filtered_iterable_dataset(
+            train_dataset, drop_no_trainable_tokens, batched=True
+        )
+    else:
+        train_dataset = train_dataset.filter(
+            drop_no_trainable_tokens,
+            batched=True,
+            **filter_map_kwargs,
+            **drop_long_kwargs,
+        )
     if prior_len:
         dropped = prior_len - len(train_dataset)
         if dropped:
@@ -472,7 +539,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             )
 
             data_loader = DataLoader(
-                train_dataset.remove_columns(["length"]),
+                train_dataset,
                 batch_sampler=sampler,
             )
             data_loader_len = len(data_loader) * cfg.micro_batch_size // cfg.batch_size
@@ -547,7 +614,7 @@ def setup_deepspeed_env(cfg, stage=None):
         if stage == 3:
             os.environ["ACCELERATE_DEEPSPEED_ZERO3_INIT"] = "true"
 
-    # NOTE(djsaunde): The distribued state cannot be initialized prior to the
+    # NOTE(djsaunde): The distributed state cannot be initialized prior to the
     # ACCELERATE_USE_DEEPSPEED assignment, but it must be initialized some time prior
     # to model load.
     if (

tests/cli/test_cli_base.py

@@ -47,7 +47,9 @@ class BaseCliTest:
         config_path = tmp_path / "config.yml"
         config_path.write_text(valid_test_config)
 
-        with patch("subprocess.run") as mock:
+        mock_fn = "os.execvpe" if command == "train" else "subprocess.run"
+
+        with patch(mock_fn) as mock:
             result = cli_runner.invoke(cli, [command, str(config_path)])
 
             assert mock.called
@@ -65,8 +67,12 @@ class BaseCliTest:
             if train:
                 expected.append("--shard=False")
 
-            assert mock.call_args.args[0] == expected
+            if command == "train":
-            assert mock.call_args.kwargs == {"check": True}
+                assert mock.call_args.args[0] == "accelerate"
+                assert mock.call_args.args[1] == expected
+            else:
+                assert mock.call_args.args[0] == expected
+                assert mock.call_args.kwargs == {"check": True}
             assert result.exit_code == 0
 
     def _test_cli_overrides(self, tmp_path: Path, valid_test_config: str):

tests/cli/test_cli_train.py

@@ -85,7 +85,7 @@ class TestTrainCommand(BaseCliTest):
         config_path = tmp_path / "config.yml"
         config_path.write_text(valid_test_config)
 
-        with patch("subprocess.run") as mock_subprocess:
+        with patch("os.execvpe") as mock_subprocess:
             result = cli_runner.invoke(
                 cli,
                 [
@@ -104,7 +104,7 @@ class TestTrainCommand(BaseCliTest):
             mock_subprocess.assert_called_once()
 
             # Verify launcher args are passed to torchrun
-            called_cmd = mock_subprocess.call_args.args[0]
+            called_cmd = mock_subprocess.call_args.args[1]
             assert called_cmd[0] == "torchrun"
             assert "--nproc_per_node=2" in called_cmd
             assert "--nnodes=1" in called_cmd
@@ -118,7 +118,7 @@ class TestTrainCommand(BaseCliTest):
         config_path = tmp_path / "config.yml"
         config_path.write_text(valid_test_config)
 
-        with patch("subprocess.run") as mock_subprocess:
+        with patch("os.execvpe") as mock_subprocess:
             result = cli_runner.invoke(
                 cli,
                 [
@@ -137,7 +137,8 @@ class TestTrainCommand(BaseCliTest):
             mock_subprocess.assert_called_once()
 
             # Verify launcher args are passed to accelerate
-            called_cmd = mock_subprocess.call_args.args[0]
+            assert mock_subprocess.call_args.args[0] == "accelerate"
+            called_cmd = mock_subprocess.call_args.args[1]
             assert called_cmd[0] == "accelerate"
             assert called_cmd[1] == "launch"
             assert "--config_file=accelerate_config.yml" in called_cmd
@@ -152,7 +153,7 @@ class TestTrainCommand(BaseCliTest):
         config_path = tmp_path / "config.yml"
         config_path.write_text(valid_test_config)
 
-        with patch("subprocess.run") as mock_subprocess:
+        with patch("os.execvpe") as mock_subprocess:
             result = cli_runner.invoke(
                 cli,
                 [
@@ -170,7 +171,8 @@ class TestTrainCommand(BaseCliTest):
             mock_subprocess.assert_called_once()
 
             # Verify no launcher args contamination
-            called_cmd = mock_subprocess.call_args.args[0]
+            assert mock_subprocess.call_args.args[0] == "accelerate"
+            called_cmd = mock_subprocess.call_args.args[1]
             assert called_cmd[0] == "accelerate"
             assert called_cmd[1] == "launch"
             # Should not contain any extra launcher args
@@ -186,7 +188,7 @@ class TestTrainCommand(BaseCliTest):
         config_path = tmp_path / "config.yml"
         config_path.write_text(valid_test_config)
 
-        with patch("subprocess.run") as mock_subprocess:
+        with patch("os.execvpe") as mock_subprocess:
             result = cli_runner.invoke(
                 cli,
                 [
@@ -207,7 +209,8 @@ class TestTrainCommand(BaseCliTest):
             assert result.exit_code == 0
             mock_subprocess.assert_called_once()
 
-            called_cmd = mock_subprocess.call_args.args[0]
+            assert mock_subprocess.call_args.args[0] == "torchrun"
+            called_cmd = mock_subprocess.call_args.args[1]
             # Verify launcher args
             assert "--nproc_per_node=8" in called_cmd
             # Verify axolotl args are also present

tests/core/test_builders.py

@@ -281,7 +281,9 @@ class TestHFRLTrainerBuilder:
         # Other settings
         assert training_arguments.dataloader_num_workers == 1
         assert training_arguments.dataloader_pin_memory is True
-        assert training_arguments.gradient_checkpointing is False
+
+        # TODO(wing): restore once trl releases 0.22.0
+        # assert training_arguments.gradient_checkpointing is True
 
     def test_dpo_training_arguments(self, dpo_cfg, model, tokenizer):
         builder = HFRLTrainerBuilder(dpo_cfg, model, tokenizer)

tests/e2e/integrations/test_kd.py

@@ -25,7 +25,7 @@ def min_cfg(temp_dir):
         "liger_rms_norm": True,
         "liger_glu_activation": True,
         "torch_compile": True,
-        "chat_template": "llama3",
+        "chat_template": "qwen3",
         "kd_trainer": True,
         "kd_ce_alpha": 0.1,
         "kd_alpha": 0.9,

tests/e2e/multigpu/test_ray.py

@@ -10,7 +10,11 @@ from accelerate.test_utils import execute_subprocess_async
 
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_tensorboard, require_torch_lt_2_6_0
+from tests.e2e.utils import (
+    check_tensorboard,
+    require_torch_2_7_0,
+    require_torch_lt_2_6_0,
+)
 
 AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
 
@@ -139,3 +143,71 @@ class TestMultiGPURay:
         check_tensorboard(
             temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
         )
+
+    @require_torch_2_7_0
+    @pytest.mark.parametrize(
+        "gradient_accumulation_steps",
+        [1, 2],
+    )
+    def test_sft_fsdp2_packed(self, temp_dir, gradient_accumulation_steps):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sample_packing": True,
+                "pad_to_sequence_len": True,
+                "sequence_len": 1024,
+                "val_set_size": 0.01,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                        "split": "train[:10%]",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 2,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": gradient_accumulation_steps,
+                "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "fsdp_version": 2,
+                "fsdp_config": {
+                    "offload_params": False,
+                    "cpu_ram_efficient_loading": False,
+                    "transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
+                    "state_dict_type": "FULL_STATE_DICT",
+                    "auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
+                    "reshard_after_forward": True,
+                },
+                "use_tensorboard": True,
+                "save_first_step": False,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "axolotl",
+                "train",
+                str(Path(temp_dir) / "config.yaml"),
+                "--use-ray",
+                "--ray-num-workers",
+                "2",
+            ]
+        )
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
+        )

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/test_streaming.py

@@ -0,0 +1,185 @@
+"""E2E tests for streaming dataset functionality"""
+
+# pylint: disable=duplicate-code
+
+import pytest
+
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import check_model_output_exists, check_tensorboard
+
+
+class TestStreamingDatasets:
+    """Test case for streaming datasets with different mixing strategies"""
+
+    @pytest.mark.parametrize(
+        ("dataset_mixing_strategy", "mixing_weights"),
+        [
+            ("round_robin", None),
+            ("weighted", [0.7, 0.3]),
+            ("random", None),
+        ],
+    )
+    def test_streaming_dataset_mixing_strategies(
+        self, temp_dir, dataset_mixing_strategy, mixing_weights
+    ):
+        """Test different mixing strategies with streaming datasets"""
+
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "flash_attention": True,
+                "sequence_len": 1024,
+                "sample_packing": False,
+                "dataset_processes": 1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                    },
+                ],
+                # Streaming config
+                "streaming": True,
+                "max_steps": 3,  # Very small for smoke test
+                "dataset_mixing_strategy": dataset_mixing_strategy,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "val_set_size": 0.0,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "use_tensorboard": True,
+                "save_first_step": False,
+            }
+        )
+
+        # Add mixing weights if specified
+        if mixing_weights:
+            cfg["mixing_weights"] = mixing_weights
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+
+        # Verify training actually happened by checking loss decrease
+        check_tensorboard(
+            temp_dir + "/runs",
+            "train/train_loss",
+            2.5,  # Loss should be reasonable for a smoke test (higher threshold for streaming)
+            "Train Loss (%s) is too high",
+        )
+
+    def test_streaming_validation_error(self, temp_dir):
+        """Test that pydantic validation catches invalid streaming configs"""
+
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                    },
+                ],
+                "streaming": True,
+                "max_steps": 3,
+                # Invalid: wrong number of weights for datasets
+                "dataset_mixing_strategy": "weighted",
+                "mixing_weights": [1.0],  # Should be [0.x, 0.y] for 2 datasets
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+            }
+        )
+
+        # This should raise a validation error
+        with pytest.raises(Exception) as exc_info:
+            validate_config(cfg)
+
+        # Verify it's the right validation error
+        assert "mixing_weights length" in str(exc_info.value)
+        assert "must match number of datasets" in str(exc_info.value)
+
+    def test_streaming_three_datasets_weighted(self, temp_dir):
+        """Test weighted mixing with three datasets"""
+
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "flash_attention": True,
+                "sequence_len": 512,
+                "sample_packing": False,
+                "dataset_processes": 1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                    },
+                    {
+                        "path": "yahma/alpaca-cleaned",
+                        "type": "alpaca",
+                    },
+                ],
+                # Streaming config
+                "streaming": True,
+                "max_steps": 3,
+                "dataset_mixing_strategy": "weighted",
+                "mixing_weights": [0.5, 0.3, 0.2],
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "val_set_size": 0.0,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "use_tensorboard": True,
+                "save_first_step": False,
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+
+        check_tensorboard(
+            temp_dir + "/runs",
+            "train/train_loss",
+            2.5,
+            "Train Loss (%s) is too high",
+        )

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_datasets.py

@@ -7,13 +7,13 @@ from typing import Any, Generator
 from unittest.mock import patch
 
 import pytest
-from datasets import Dataset
+from datasets import Dataset, IterableDataset
 from huggingface_hub import snapshot_download
 from transformers import PreTrainedTokenizer
 
 from axolotl.loaders.tokenizer import load_tokenizer
 from axolotl.utils.data.rl import prepare_preference_datasets
-from axolotl.utils.data.sft import _load_tokenized_prepared_datasets
+from axolotl.utils.data.sft import _load_tokenized_prepared_datasets, prepare_datasets
 from axolotl.utils.dict import DictDefault
 
 from tests.constants import (
@@ -24,6 +24,7 @@ from tests.constants import (
 from tests.hf_offline_utils import enable_hf_offline
 
 
+# pylint: disable=too-many-public-methods
 class TestDatasetPreparation:
     """Test a configured dataloader."""
 
@@ -46,6 +47,24 @@ class TestDatasetPreparation:
             ]
         )
 
+    @pytest.fixture
+    def streaming_dataset_fixture(self):
+        """Create a streaming dataset fixture for testing."""
+
+        def generator():
+            yield {
+                "instruction": "Evaluate this sentence for spelling and grammar mistakes",
+                "input": "He finnished his meal and left the resturant",
+                "output": "He finished his meal and left the restaurant.",
+            }
+            yield {
+                "instruction": "What is the capital of France?",
+                "input": "",
+                "output": "The capital of France is Paris.",
+            }
+
+        return IterableDataset.from_generator(generator)
+
     @pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
     @enable_hf_offline
     def test_load_hub(self, tokenizer):
@@ -486,3 +505,162 @@ class TestDatasetPreparation:
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
             shutil.rmtree(tmp_ds_path)
+
+    def test_streaming_sft_dataset(self, tokenizer, streaming_dataset_fixture):
+        """Test streaming SFT dataset preparation with IterableDataset."""
+        with patch("axolotl.utils.data.sft.load_dataset_with_config") as mock_load:
+            mock_load.return_value = streaming_dataset_fixture
+
+            cfg = DictDefault(
+                {
+                    "tokenizer_config": "huggyllama/llama-7b",
+                    "sequence_len": 256,
+                    "streaming": True,
+                    "max_steps": 100,  # Required for streaming datasets
+                    "datasets": [
+                        {
+                            "path": "dummy/path",
+                            "type": "alpaca",
+                        },
+                    ],
+                }
+            )
+
+            train_dataset, eval_dataset, total_num_steps, prompters = prepare_datasets(
+                cfg, tokenizer
+            )
+
+            # Verify it returns an IterableDataset
+            assert isinstance(train_dataset, IterableDataset)
+            assert eval_dataset is None  # No eval split for streaming
+            assert total_num_steps == 100  # Should use max_steps
+            assert len(prompters) == 1
+
+            # Test that we can iterate through the dataset
+            sample_count = 0
+            for sample in train_dataset:
+                assert "input_ids" in sample
+                assert "attention_mask" in sample
+                assert "labels" in sample
+                sample_count += 1
+                if sample_count >= 2:  # Just test first few samples
+                    break
+
+            assert sample_count == 2
+
+    def test_dataset_mixing_strategy_validation(self):
+        """Test validation of dataset mixing strategy configuration."""
+        from axolotl.utils.data.shared import _merge_datasets_with_strategy
+
+        # Test valid strategies work
+        valid_strategies = ["round_robin", "weighted", "random"]
+        dataset1 = Dataset.from_dict({"text": ["a"], "source": ["ds1"]})
+        dataset2 = Dataset.from_dict({"text": ["b"], "source": ["ds2"]})
+
+        for strategy in valid_strategies:
+            cfg = DictDefault(
+                {
+                    "dataset_mixing_strategy": strategy,
+                    "mixing_weights": [0.5, 0.5] if strategy == "weighted" else None,
+                    "seed": 42,
+                }
+            )
+            # Should not raise an error
+            merged = _merge_datasets_with_strategy([dataset1, dataset2], cfg)
+            assert len(merged) >= 1
+
+    def test_regular_dataset_round_robin_mixing(self):
+        """Test round-robin mixing for regular datasets."""
+        from axolotl.utils.data.shared import _merge_datasets_with_strategy
+
+        # Create test datasets
+        dataset1 = Dataset.from_dict(
+            {"text": ["ds1_item1", "ds1_item2"], "source": ["ds1", "ds1"]}
+        )
+        dataset2 = Dataset.from_dict(
+            {"text": ["ds2_item1", "ds2_item2"], "source": ["ds2", "ds2"]}
+        )
+
+        cfg = DictDefault({"dataset_mixing_strategy": "round_robin", "seed": 42})
+
+        merged = _merge_datasets_with_strategy([dataset1, dataset2], cfg)
+
+        # Should have all samples from both datasets
+        assert len(merged) == 4
+        assert isinstance(merged, Dataset)
+
+        # Check that samples are interleaved (not just concatenated)
+        sources = [sample["source"] for sample in merged]
+        # Round-robin should alternate between datasets
+        assert sources != ["ds1", "ds1", "ds2", "ds2"]  # Not concatenated
+
+    def test_regular_dataset_weighted_mixing(self):
+        """Test weighted mixing for regular datasets."""
+        from axolotl.utils.data.shared import _merge_datasets_with_strategy
+
+        # Create test datasets
+        dataset1 = Dataset.from_dict(
+            {
+                "text": ["ds1_item1", "ds1_item2", "ds1_item3", "ds1_item4"],
+                "source": ["ds1"] * 4,
+            }
+        )
+        dataset2 = Dataset.from_dict(
+            {
+                "text": ["ds2_item1", "ds2_item2", "ds2_item3", "ds2_item4"],
+                "source": ["ds2"] * 4,
+            }
+        )
+
+        cfg = DictDefault(
+            {
+                "dataset_mixing_strategy": "weighted",
+                "mixing_weights": [0.75, 0.25],  # 3:1 ratio
+                "seed": 42,
+            }
+        )
+
+        merged = _merge_datasets_with_strategy([dataset1, dataset2], cfg)
+
+        # Should have samples proportional to weights
+        assert len(merged) > 0
+        assert isinstance(merged, Dataset)
+
+        # Count samples from each dataset
+        sources = [sample["source"] for sample in merged]
+        ds1_count = sources.count("ds1")
+        ds2_count = sources.count("ds2")
+
+        # Should have samples from both datasets
+        assert ds1_count > 0 and ds2_count > 0  # Both datasets should be represented
+
+    def test_streaming_dataset_mixing(self):
+        """Test that streaming datasets use HuggingFace interleave_datasets."""
+        from axolotl.utils.data.shared import _merge_datasets_with_strategy
+
+        # Create test streaming datasets
+        def gen1():
+            yield {"text": "stream1_item1", "source": "stream1"}
+            yield {"text": "stream1_item2", "source": "stream1"}
+
+        def gen2():
+            yield {"text": "stream2_item1", "source": "stream2"}
+            yield {"text": "stream2_item2", "source": "stream2"}
+
+        stream1 = IterableDataset.from_generator(gen1)
+        stream2 = IterableDataset.from_generator(gen2)
+
+        cfg = DictDefault({"dataset_mixing_strategy": "round_robin", "seed": 42})
+
+        merged = _merge_datasets_with_strategy([stream1, stream2], cfg)
+
+        # Should return an IterableDataset
+        assert isinstance(merged, IterableDataset)
+
+        # Test that we can iterate and get samples
+        samples = list(merged.take(3))
+        assert len(samples) >= 2  # Should get at least 2 samples
+
+        # Should have samples from both datasets
+        sources = [sample["source"] for sample in samples]
+        assert len(set(sources)) >= 1  # At least one unique source

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
 
@@ -70,7 +70,7 @@ 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(

tests/test_packed_dataset.py

@@ -1,16 +1,11 @@
 """Module for testing dataset sequence packing"""
 
 import unittest
-from pathlib import Path
 
-from datasets import Dataset, load_dataset
 from transformers import AutoTokenizer
 
 from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
-from axolotl.datasets import ConstantLengthDataset, TokenizedPromptDataset
-from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
-from axolotl.prompters import AlpacaPrompter
 from axolotl.train import setup_model_and_trainer
 from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.dict import DictDefault
@@ -36,43 +31,6 @@ class TestPacking(unittest.TestCase):
             }
         )
 
-    def test_increments_attention(self):
-        prompter = AlpacaPrompter("chat")
-        strat = AlpacaPromptTokenizingStrategy(
-            prompter,
-            self.tokenizer,
-            False,
-            2048,
-        )
-        dateset = load_dataset(
-            "json",
-            data_files=str(Path(__file__).parent / "fixtures/alpaca/alpaca.json"),
-        )["train"]
-        dataset = Dataset.from_list(list(TokenizedPromptDataset(strat, dateset)))
-
-        constant_len_dataset = ConstantLengthDataset(
-            self.tokenizer,
-            [dataset],
-            seq_length=2048,
-        )
-        packed_dataset = Dataset.from_list(list(constant_len_dataset))
-        example = packed_dataset[0]
-        next_bos_index = (
-            example["input_ids"][1:].index(self.tokenizer.bos_token_id) + 1
-        )  # add one since we sliced
-
-        # first example doesn't have mask reset
-        assert example["input_ids"][0] == self.tokenizer.bos_token_id
-        assert example["attention_mask"][0] == 1
-        assert example["position_ids"][0] == 0
-        assert example["position_ids"][1] == 1
-
-        # but subsequent one does
-        assert example["input_ids"][next_bos_index] == self.tokenizer.bos_token_id
-        assert example["attention_mask"][next_bos_index] == 2
-        assert example["position_ids"][next_bos_index] == 0
-        assert example["position_ids"][next_bos_index + 1] == 1
-
     @with_temp_dir
     def test_lora_packing(self, temp_dir):
         # pylint: disable=duplicate-code

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")