Merge branch 'main' into testingci

.github/CONTRIBUTING.md

@@ -57,13 +57,6 @@ 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
-
 ## Style Guidelines
 
 ### Code Style

.github/workflows/base.yml

@@ -54,7 +54,7 @@ jobs:
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
           - cuda: "128"
-            cuda_version: 12.8.1
+            cuda_version: 12.6.3
             cudnn_version: ""
             python_version: "3.11"
             pytorch: 2.7.1
@@ -64,16 +64,9 @@ jobs:
             cuda_version: 12.8.1
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.8.0
+            pytorch: nightly
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
+            dockerfile: "Dockerfile-base-nightly"
-#          - cuda: "128"
-#            cuda_version: 12.8.1
-#            cudnn_version: ""
-#            python_version: "3.11"
-#            pytorch: nightly
-#            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-#            dockerfile: "Dockerfile-base-nightly"
 #          # "next" is for release candidates of pytorch
 #          - cuda: "128"
 #            cuda_version: 12.8.1
@@ -129,13 +122,6 @@ jobs:
             pytorch: 2.6.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
-          - cuda: "126"
-            cuda_version: 12.6.3
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.7.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -143,13 +129,6 @@ jobs:
             pytorch: 2.7.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
-          - cuda: "128"
-            cuda_version: 12.8.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.8.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/main.yml

@@ -24,13 +24,12 @@ jobs:
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.0
-            axolotl_extras:
+            axolotl_extras: vllm
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.1
-            axolotl_extras: vllm
+            axolotl_extras:
-            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -98,12 +97,6 @@ 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
@@ -157,18 +150,6 @@ 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/multi-gpu-e2e.yml

@@ -37,14 +37,14 @@ jobs:
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.0
-            axolotl_extras:
+            axolotl_extras: vllm
             num_gpus: 2
             nightly_build: "true"
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.1
-            axolotl_extras: vllm
+            axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
     runs-on: [self-hosted, modal]

.github/workflows/preview-docs.yml

@@ -53,7 +53,6 @@ jobs:
 
       - name: Netlify Publish
         uses: nwtgck/actions-netlify@v3.0
-        if: ${{ github.event.pull_request.head.repo.full_name == github.repository }}
         id: netlify
         with:
           publish-dir: './_site'

.github/workflows/tests.yml

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

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v6.0.0
+    rev: v5.0.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
@@ -23,11 +23,11 @@ repos:
     hooks:
     - id: flake8
 -   repo: https://github.com/pylint-dev/pylint
-    rev: v3.3.8
+    rev: v3.3.7
     hooks:
     - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.17.1
+    rev: v1.17.0
     hooks:
     - id: mypy
       additional_dependencies:

.runpod/README.md

@@ -119,15 +119,14 @@ datasets:
 
 ## Dataset Processing
 
-| Option                            | Default                    | Description                         |
+| Option                        | Default                    | Description                       |
-| --------------------------------- | -------------------------- | ----------------------------------- |
+| ----------------------------- | -------------------------- | --------------------------------- |
-| `dataset_prepared_path`           | `"data/last_run_prepared"` | Path for prepared dataset           |
+| `dataset_prepared_path`       | `"data/last_run_prepared"` | Path for prepared dataset         |
-| `push_dataset_to_hub`             | `""`                       | Push dataset to HF hub              |
+| `push_dataset_to_hub`         | `""`                       | Push dataset to HF hub            |
-| `dataset_processes`               | `4`                        | Number of preprocessing processes   |
+| `dataset_processes`           | `4`                        | Number of preprocessing processes |
-| `dataset_keep_in_memory`          | `false`                    | Keep dataset in memory              |
+| `dataset_keep_in_memory`      | `false`                    | Keep dataset in memory            |
-| `shuffle_merged_datasets`         | `true`                     | Shuffle merged datasets             |
+| `shuffle_merged_datasets`     | `true`                     | Shuffle merged datasets           |
-| `shuffle_before_merging_datasets` | `false`                    | Shuffle each dataset before merging |
+| `dataset_exact_deduplication` | `true`                     | Deduplicate datasets              |
-| `dataset_exact_deduplication`     | `true`                     | Deduplicate datasets                |
 
 ## LoRA Configuration
 
@@ -185,6 +184,7 @@ datasets:
 | `flash_attention`          | `false` | Use flash attention           |
 | `flash_attn_cross_entropy` | `false` | Flash attention cross entropy |
 | `flash_attn_rms_norm`      | `false` | Flash attention RMS norm      |
+| `flash_attn_fuse_qkv`      | `false` | Fuse QKV operations           |
 | `flash_attn_fuse_mlp`      | `false` | Fuse MLP operations           |
 | `sdp_attention`            | `false` | Use scaled dot product        |
 | `s2_attention`             | `false` | Use shifted sparse attention  |

.runpod/src/config/config.yaml

@@ -296,6 +296,7 @@
 # flash_attention:
 # flash_attn_cross_entropy:  # Whether to use flash-attention cross entropy implementation - advanced use only
 # flash_attn_rms_norm:  # Whether to use flash-attention rms norm implementation - advanced use only
+# flash_attn_fuse_qkv: # Whether to fuse QKV into a single operation
 # flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
 # # Whether to use scaled-dot-product attention
 # # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
@@ -540,6 +541,7 @@ xformers_attention: ${XFORMERS_ATTENTION}
 flash_attention: ${FLASH_ATTENTION}
 flash_attn_cross_entropy: ${FLASH_ATTN_CROSS_ENTROPY}
 flash_attn_rms_norm: ${FLASH_ATTN_RMS_NORM}
+flash_attn_fuse_qkv: ${FLASH_ATTN_FUSE_QKV}
 flash_attn_fuse_mlp: ${FLASH_ATTN_FUSE_MLP}
 sdp_attention: ${SDP_ATTENTION}
 s2_attention: ${S2_ATTENTION}

CITATION.cff

@@ -1,10 +0,0 @@
-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

@@ -25,28 +25,15 @@
 
 ## 🎉 Latest Updates
 
-- 2025/07:
-  - ND Parallelism support has been added into Axolotl. Compose Context Parallelism (CP), Tensor Parallelism (TP), and Fully Sharded Data Parallelism (FSDP) within a single node and across multiple nodes. Check out the [blog post](https://huggingface.co/blog/accelerate-nd-parallel) for more info.
-  - Axolotl adds more models: [GPT-OSS](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gpt-oss), [Gemma 3n](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gemma3n), [Liquid Foundation Model 2 (LFM2)](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/lfm2), and [Arcee Foundation Models (AFM)](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/afm).
-  - FP8 finetuning with fp8 gather op is now possible in Axolotl via `torchao`. Get started [here](https://docs.axolotl.ai/docs/mixed_precision.html#sec-fp8)!
-  - [Voxtral](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/voxtral), [Magistral 1.1](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral), and [Devstral](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/devstral) with mistral-common tokenizer support has been integrated in Axolotl!
-  - TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst) for using ALST with Axolotl!
-- 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more!
-- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning.
-
-<details>
-
-<summary>Expand older updates</summary>
-
 - 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral) to start training your own Magistral models with Axolotl!
+- 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more!
 - 2025/04: Llama 4 support has been added in Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-4) to start training your own Llama 4 models with Axolotl's linearized version!
+- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning.
 - 2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the [docs](https://docs.axolotl.ai/docs/multimodal.html) to fine-tune your own!
 - 2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the [docs](https://docs.axolotl.ai/docs/lora_optims.html) to give it a try.
 - 2025/02: Axolotl has added GRPO support. Dive into our [blog](https://huggingface.co/blog/axolotl-ai-co/training-llms-w-interpreter-feedback-wasm) and [GRPO example](https://github.com/axolotl-ai-cloud/grpo_code) and have some fun!
 - 2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See [docs](https://docs.axolotl.ai/docs/reward_modelling.html).
 
-</details>
-
 ## ✨ Overview
 
 Axolotl is a tool designed to streamline post-training for various AI models.
@@ -92,20 +79,6 @@ docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
 
 Other installation approaches are described [here](https://docs.axolotl.ai/docs/installation.html).
 
-#### Cloud Providers
-
-<details>
-
-- [RunPod](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
-- [Vast.ai](https://cloud.vast.ai?ref_id=62897&template_id=bdd4a49fa8bce926defc99471864cace&utm_source=github&utm_medium=developer_community&utm_campaign=template_launch_axolotl&utm_content=readme)
-- [PRIME Intellect](https://app.primeintellect.ai/dashboard/create-cluster?image=axolotl&location=Cheapest&security=Cheapest&show_spot=true)
-- [Modal](https://www.modal.com?utm_source=github&utm_medium=github&utm_campaign=axolotl)
-- [Novita](https://novita.ai/gpus-console?templateId=311)
-- [JarvisLabs.ai](https://jarvislabs.ai/templates/axolotl)
-- [Latitude.sh](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
-
-</details>
-
 ### Your First Fine-tune
 
 ```bash
@@ -147,22 +120,14 @@ Contributions are welcome! Please see our [Contributing Guide](https://github.co
 
 ## ❤️ Sponsors
 
+Thank you to our sponsors who help make Axolotl possible:
+
+- [Modal](https://www.modal.com?utm_source=github&utm_medium=github&utm_campaign=axolotl) - Modal lets you run
+jobs in the cloud, by just writing a few lines of Python. Customers use Modal to deploy Gen AI models at large scale,
+fine-tune large language models, run protein folding simulations, and much more.
+
 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

@@ -0,0 +1,10 @@
+# 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

_quarto.yml

@@ -35,30 +35,25 @@ quartodoc:
         - cli.train
         - cli.evaluate
         - cli.args
-        - cli.art
         - cli.checks
         - cli.config
-        - cli.delinearize_llama4
         - cli.inference
         - cli.merge_lora
         - cli.merge_sharded_fsdp_weights
         - cli.preprocess
-        - cli.quantize
+        - cli.sweeps
+        - cli.utils
         - cli.vllm_serve
         - cli.cloud.base
         - cli.cloud.modal_
-        - cli.utils
+        - cli.quantize
-        - cli.utils.args
-        - cli.utils.fetch
-        - cli.utils.load
-        - cli.utils.sweeps
-        - cli.utils.train
     - title: Trainers
       desc: Training implementations
       contents:
         - core.trainers.base
         - core.trainers.trl
         - core.trainers.mamba
+        - core.trainers.relora
         - core.trainers.dpo.trainer
         - core.trainers.grpo.trainer
         - core.trainers.grpo.sampler
@@ -274,7 +269,7 @@ website:
             - docs/dataset_preprocessing.qmd
             - docs/multipack.qmd
             - docs/mixed_precision.qmd
-            - docs/optimizers.qmd
+            - docs/gradient_accumulation.qmd
 
         - section: "Advanced Features"
           contents:
@@ -284,7 +279,6 @@ website:
             - docs/custom_integrations.qmd
             - docs/sequence_parallelism.qmd
             - docs/gradient_checkpointing.qmd
-            - docs/nd_parallelism.qmd
 
         - section: "Troubleshooting"
           contents:

cicd/multigpu.sh

@@ -2,7 +2,7 @@
 set -e
 
 # Only run two tests at a time to avoid OOM on GPU (with coverage collection)
-pytest -v --durations=10 -n2 \
+pytest -v -n2 \
   --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/ \
   --ignore=/workspace/axolotl/tests/e2e/multigpu/patched/ \
   /workspace/axolotl/tests/e2e/multigpu/ \
@@ -19,7 +19,5 @@ pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/patched/ \
   --cov-append \
   --cov-report=xml:multigpu-coverage.xml
 
-# Upload coverage to Codecov if CODECOV_TOKEN is available
+# Upload coverage to Codecov
-if [ -n "$CODECOV_TOKEN" ]; then
+codecov upload-process -t "${CODECOV_TOKEN}" -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION} || true
-  codecov upload-process -t "${CODECOV_TOKEN}" -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION} || true
-fi

cicd/single_gpu.py

@@ -65,9 +65,6 @@ GPU_CONFIG = f"L40S:{N_GPUS}"
 def run_cmd(cmd: str, run_folder: str):
     import subprocess  # nosec
 
-    sp_env = os.environ.copy()
-    sp_env["AXOLOTL_DATASET_PROCESSES"] = "8"
-
     # Propagate errors from subprocess.
-    if exit_code := subprocess.call(cmd.split(), cwd=run_folder, env=sp_env):  # nosec
+    if exit_code := subprocess.call(cmd.split(), cwd=run_folder):  # nosec
         exit(exit_code)  # pylint: disable=consider-using-sys-exit

docker/Dockerfile-base

@@ -16,10 +16,7 @@ ENV PYTHON_VERSION=$PYTHON_VERSION
 ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
 
 RUN apt-get update \
-    && apt-get install -y --no-install-recommends \
+    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config \
-        wget git build-essential ninja-build git-lfs libaio-dev pkg-config \
-        ibverbs-providers ibverbs-utils infiniband-diags  \
-        librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm \
     && rm -rf /var/cache/apt/archives \
     && rm -rf /var/lib/apt/lists/* \
     && wget \

docker/Dockerfile-cloud

@@ -15,7 +15,7 @@ COPY scripts/motd /etc/motd
 RUN pip install jupyterlab notebook ipywidgets && \
     jupyter lab clean
 RUN apt update && \
-    apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop && \
+    apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm && \
     rm -rf /var/cache/apt/archives && \
     rm -rf /var/lib/apt/lists/* && \
     mkdir -p ~/.ssh && \

docker/Dockerfile-cloud-no-tmux

@@ -9,15 +9,13 @@ ENV HF_HUB_ENABLE_HF_TRANSFER="1"
 EXPOSE 8888
 EXPOSE 22
 
-COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh
+COPY scripts/cloud-entrypoint-term.sh /root/cloud-entrypoint.sh
 COPY scripts/motd /etc/motd
 
 RUN pip install jupyterlab notebook ipywidgets && \
     jupyter lab clean
-RUN apt update && \
+RUN apt install --yes --no-install-recommends openssh-server tmux sudo && \
-    apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm && \
+    pip3 install -U --no-cache-dir grpcio ray[default]==2.9.3 && \
-    rm -rf /var/cache/apt/archives && \
-    rm -rf /var/lib/apt/lists/* && \
     mkdir -p ~/.ssh && \
     chmod 700 ~/.ssh && \
     printf "[ ! -z \"\$TERM\" -a -r /etc/motd ] && cat /etc/motd\n" >> ~/.bashrc && \

docs/cli.qmd

@@ -23,20 +23,6 @@ axolotl <command> [config.yml] [options]
 
 The config file can be local or a URL to a raw YAML file.
 
-### Launcher Arguments
-
-For commands that support multi-GPU (`train`, `evaluate`, ...), you can pass launcher-specific arguments using the `--` separator:
-
-```bash
-# Pass torchrun arguments
-axolotl train config.yml --launcher torchrun -- --nproc_per_node=2 --nnodes=1
-
-# Pass accelerate arguments
-axolotl train config.yml --launcher accelerate -- --config_file=accelerate_config.yml --num_processes=4
-```
-
-Arguments after `--` are passed directly to the launcher (torchrun, accelerate launch, etc.).
-
 ## Command Reference
 
 ### fetch
@@ -94,11 +80,7 @@ axolotl train config.yml \
     --num-epochs 3
 
 # Training without accelerate
-axolotl train config.yml --launcher python
+axolotl train config.yml --no-accelerate
-
-# Pass launcher-specific arguments using -- separator
-axolotl train config.yml --launcher torchrun -- --nproc_per_node=2 --nnodes=1
-axolotl train config.yml --launcher accelerate -- --config_file=accelerate_config.yml
 
 # Resume training from checkpoint
 axolotl train config.yml --resume-from-checkpoint path/to/checkpoint
@@ -193,9 +175,6 @@ Evaluates a model's performance (loss etc) on the train and eval datasets.
 ```bash
 # Basic evaluation
 axolotl evaluate config.yml
-
-# Evaluation with launcher arguments
-axolotl evaluate config.yml --launcher torchrun -- --nproc_per_node=2
 ```
 
 ### lm-eval
@@ -308,6 +287,9 @@ axolotl preprocess config.yml --cloud cloud_config.yml
 # Train on cloud
 axolotl train config.yml --cloud cloud_config.yml
 
+# Train without accelerate on cloud
+axolotl train config.yml --cloud cloud_config.yml --no-accelerate
+
 # Run lm-eval on cloud
 axolotl lm-eval config.yml --cloud cloud_config.yml
 ```

docs/dataset-formats/conversation.qmd

@@ -212,11 +212,10 @@ Instead of passing `tools` via the system prompt, an alternative method would be
 Tools need to follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
 :::
 
-Example config for Llama4:
 ```yaml
 chat_template: llama4
 datasets:
-  - path: Nanobit/text-tools-2k-test
+  - path: ...
     type: chat_template
     # field_tools: tools # default is `tools`
 ```

docs/faq.qmd

@@ -136,7 +136,3 @@ description: Frequently asked questions
 >   dynamic: false
 >   mode: max-autotune-no-cudagraphs
 > ```
-
-**Q: `ValueError("Backward pass should have cleared tracker of all tensors")`
-
-> A: This may happen due to edge cases in using the modern OffloadActivations context manager for CUDA streams. If you encounter this error, you may have success using the naive implementation with `offload_activations: legacy` in your YAML.

docs/installation.qmd

@@ -124,13 +124,10 @@ For providers supporting Docker:
 
 - Use `axolotlai/axolotl-cloud:main-latest`
 - Available on:
-    - [RunPod](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
+  - [Latitude.sh](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
-    - [Vast.ai](https://cloud.vast.ai?ref_id=62897&template_id=bdd4a49fa8bce926defc99471864cace&utm_source=axolotl&utm_medium=partner&utm_campaign=template_launch_july2025&utm_content=docs_link)
+  - [JarvisLabs.ai](https://jarvislabs.ai/templates/axolotl)
-    - [PRIME Intellect](https://app.primeintellect.ai/dashboard/create-cluster?image=axolotl&location=Cheapest&security=Cheapest&show_spot=true)
+  - [RunPod](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
-    - [Modal](https://www.modal.com?utm_source=github&utm_medium=github&utm_campaign=axolotl)
+  - [Novita](https://novita.ai/gpus-console?templateId=311)
-    - [Novita](https://novita.ai/gpus-console?templateId=311)
-    - [JarvisLabs.ai](https://jarvislabs.ai/templates/axolotl)
-    - [Latitude.sh](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
 
 ### Google Colab {#sec-colab}
 

docs/multi-node.qmd

@@ -69,19 +69,11 @@ export NCCL_BUFFSIZE=2097152
 
 Run the following on each node:
 
-### Option 1: New Axolotl CLI with launcher args (Recommended)
-
-```bash
-axolotl train config.yaml --launcher torchrun -- --nnodes $num_nodes --nproc_per_node $gpu_per_node --rdzv_id $rdzv_id --rdzv_backend c10d --rdzv_endpoint "$head_node_ip:$head_node_port"
-```
-
-### Option 2: Direct torchrun (Legacy)
-
 ```bash
 torchrun --nnodes $num_nodes --nproc_per_node $gpu_per_node --rdzv_id $rdzv_id --rdzv_backend c10d --rdzv_endpoint "$head_node_ip:$head_node_port" -m axolotl.cli.train config.yaml
 ```
 
-Please make sure to substitute the placeholder variables:
+Please make sure to substitute the placeholder variables.
 
 - `num_nodes`: Number of nodes (containing GPUs)
 - `gpu_per_node`: Number of gpus per node
@@ -89,6 +81,8 @@ Please make sure to substitute the placeholder variables:
 - `head_node_port`: Port of the head node (make sure other machines can connect to this. Default 29400)
 - `rdzv_id`: A unique job ID that is used by the job across nodes.
 
-The new CLI approach (Option 1) is recommended as it provides consistent argument handling and works seamlessly with other Axolotl CLI features.
+::: {.callout-note}
+You need to call `axolotl.cli.train` instead of `axolotl train` as the latter calls accelerate under the hood
+:::
 
 More info on the available configs can be found on the Pytorch docs [here](https://pytorch.org/docs/stable/elastic/run.html)

docs/nd_parallelism.qmd

@@ -1,108 +0,0 @@
----
-title: "N-D Parallelism (Beta)"
----
-
-Axolotl enables training models at scale by composing different parallelism techniques. This is essential when:
-
-- A model's weights are too large to fit on a single GPU's memory.
-- A model's activations, especially with very long contexts, are too large for a single GPU.
-- You want to accelerate training by using multiple GPUs or nodes.
-
-or combinations of the above!
-
-## Core Concepts
-
-Parallelism strategies can be combined. The key is understanding how each one divides the workload. PyTorch's `DeviceMesh` is the modern way to manage these combinations, creating a logical grid of your GPUs and assigning different parallel strategies to different dimensions of the grid.
-
-### Data Parallelism {#sec-dp}
-
-Data Parallelism focuses on splitting the global data batch across GPUs.
-
-- Distributed Data Parallel (DDP): The classic approach. The full model is replicated on every GPU. Each GPU processes a different slice of the data batch. Gradients are then averaged across all GPUs after the backward pass to keep the models synchronized. This can substantially improve data throughput compared to single-device training, but requires that each GPU is able to hold the entire model, its gradients, and optimizer states.
-
-- [Fully Sharded Data Parallel (FSDP)](multi-gpu.qmd#fully-sharded-data-parallel-(fsdp)): A highly memory-efficient form of data parallelism (inspired by DeepSpeed's ZeRO). Instead of replicating the model, FSDP shards the model's *parameters, gradients, and optimizer states* across the GPUs in the data-parallel group. During computation, each GPU receives the specific parameters it needs via an `all_gather` operation just before they are used, and they can be discarded immediately after (`reshard-after-forward`).
-    - FSDP maps to ZeRO stages:
-        - ZeRO-2 (`reshard_after_forward=False`): Shards gradients and optimizer states. Model weights are replicated on each GPU.
-        - ZeRO-3 (`reshard_after_forward=True`): Shards gradients, optimizer states, AND model parameters. This provides the most memory savings at the cost of more communication (re-gathering parameters for both forward and backward passes).
-
-### [Experimental] Tensor Parallelism (TP) {#sec-tp}
-
-Also known as "horizontal model parallelism," as described in the [Megatron-LM paper](https://arxiv.org/pdf/1909.08053.pdf). Instead of splitting the batch, TP splits the model's layers themselves across GPUs.
-
-- How it works: For a linear layer `Y = XA`, the weight matrix `A` is split column-wise (`A = [A_1, A_2]`). The computation becomes `Y_1 = XA_1` and `Y_2 = XA_2`, which can happen in parallel on different GPUs. The final output `Y` is simply the concatenation of `Y_1` and `Y_2`. Check [this comment](https://github.com/huggingface/transformers/issues/10321#issuecomment-783543530) for more detailed info.
-- Requirement: TP involves frequent, small communications within a forward/backward pass. It requires a very fast interconnect between GPUs (e.g., NVLink) and is typically not recommended across different nodes.
-
-### Context Parallelism (CP) {#sec-cp}
-
-Context Parallelism, also called [Sequence Parallelism](sequence_parallelism.qmd), addresses the memory bottleneck from long sequences. The input sequence itself is split along the sequence length dimension and distributed across GPUs.
-
-- How it works: If you have a sequence of 8192 tokens and a `context_parallel_size` of 4, each GPU will only handle a chunk of 2048 tokens.
-- The Challenge: Attention is not local; every token needs to "attend to" every other token. Splitting the sequence breaks this.
-- The Solution (`ring-flash-attention`): An efficient communication protocol is used. To compute attention for its local sequence chunk, each GPU passes its Key-Value (KV) cache to its neighbor in a "ring." After `N-1` steps, every GPU has seen the KV-cache from all other GPUs, allowing it to compute the correct attention values for its chunk. This is implemented using the highly optimized `flash-attention` kernel at each step.
-
-### Hybrid Sharding Data Parallel (HSDP) {#sec-hsdp}
-
-HSDP is a 2D strategy that intelligently combines FSDP and DDP, typically for multi-node training.
-
-- Intra-Node (within a machine): Use FSDP. This is efficient because GPUs on the same node have fast interconnects (NVLink), making the `all_gather` operations for sharded parameters fast.
-- Inter-Node (across machines): Use DDP. The gradient synchronization between nodes is less frequent than FSDP's parameter gathering, making it a better fit for the slower node-to-node network (e.g., Ethernet/Infiniband).
-- Example: With 2 nodes of 8 GPUs each (16 total), you could have `dp_shard_size=8` (FSDP within each node) and `dp_replicate_size=2` (DDP across the two nodes).
-
-## Usage
-
-```yaml
-# FSDP config. See https://docs.axolotl.ai/docs/multi-gpu.html#sec-fsdp
-fsdp_version: 2
-fsdp_config:
-  # ...
-
-# The number of GPUs to shard the model parameters across (FSDP dimension).
-dp_shard_size: 4
-
-# The number of times to replicate the sharded model (DDP dimension).
-dp_replicate_size: 2
-
-# Number of GPUs for Tensor Parallelism.
-tensor_parallel_size: 1  # (default is 1, no TP)
-
-# Number of GPUs for Context/Sequence Parallelism.
-context_parallel_size: 1 # (default is 1, no CP)
-```
-
-Note: We recommend FSDP. DeepSpeed is only compatible with `tensor_parallel_size`.
-
-## Examples
-
-::: {.callout-tip}
-See our example configs [here](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/distributed-parallel).
-:::
-
-1.  HSDP on 2 nodes with 4 GPUs each (8 GPUs total):
-    - You want FSDP within each node and DDP across nodes.
-    - Set `dp_shard_size: 4` and `dp_replicate_size: 2`.
-
-2.  FSDP + TP on a single 8-GPU node:
-    - You want to split the model across 4 GPUs using FSDP, and further split each layer across 2 GPUs with TP.
-    - Set `dp_shard_size: 4` and `tensor_parallel_size: 2`.
-
-3.  FSDP + CP on a single 8-GPU node for long context:
-    - You want to shard the model across all 8 GPUs and also split the sequence length across all 8 GPUs.
-    - Set `dp_shard_size: 8` and `context_parallel_size: 8`. Note: this means the data parallel group and context parallel group are the same. A more common setup might be to shard across a smaller group.
-
-## Support Matrix
-
-This matrix describes how different parallelism methods can be combined in Axolotl.
-
-| Combination | `dp_replicate_size` | `dp_shard_size` | `tp_size` | `cp_size` | Status & Notes |
-| --- | :---: | :---: |:---:|:---:|---|
-| **FSDP** (ZeRO-3) | 1 | >1 | 1 | 1 | ✅ Fully supported. Shards model across all GPUs. |
-| **HSDP** | >1 | >1 | 1 | 1 | ✅ Fully supported. FSDP intra-node, DDP inter-node. |
-| **FSDP + TP** | 1 | >1 | >1 | 1 | ✅ **2D Parallelism**. Shards the model across a `dp_shard` group, and TP-splits layers within the `tp` group. |
-| **HSDP + TP** | >1 | >1 | >1 | 1 | ✅ **3D Parallelism**. A powerful but complex combination. |
-| **FSDP + CP** | 1 | >1 | 1 | >1 | ✅ **2D Parallelism**. Combines FSDP with context parallelism. |
-| **FSDP + TP + CP**| 1 | >1 | >1| >1| ✅ **3D Parallelism**. Another advanced combination. |
-| DDP + TP/CP | >1 | 1 | >1 | >1 | ❌ **Not Supported**. The `ParallelismConfig` explicitly prevents this, as composing pure DDP with TP or CP is currently not supported. You should use FSDP + TP/CP instead (`dp_shard_size > 1`). |
-| Just TP / CP | 1 | 1 | >1 | >1 | ✅ Supported. Useful for inference or when the model fits on one GPU but context is too long. |
-
-- `tp_size` refers to `tensor_parallel_size`
-- `cp_size` refers to `context_parallel_size`

docs/optimizers.qmd

@@ -1,129 +0,0 @@
----
-title: Optimizers
-description: Configuring optimizers
----
-
-## Overview
-
-Axolotl supports all optimizers supported by [transformers OptimizerNames](https://github.com/huggingface/transformers/blob/51f94ea06d19a6308c61bbb4dc97c40aabd12bad/src/transformers/training_args.py#L142-L187)
-
-Here is a list of optimizers supported by transformers as of `v4.54.0`:
-
-- `adamw_torch`
-- `adamw_torch_fused`
-- `adamw_torch_xla`
-- `adamw_torch_npu_fused`
-- `adamw_apex_fused`
-- `adafactor`
-- `adamw_anyprecision`
-- `adamw_torch_4bit`
-- `adamw_torch_8bit`
-- `ademamix`
-- `sgd`
-- `adagrad`
-- `adamw_bnb_8bit`
-- `adamw_8bit`  # alias for adamw_bnb_8bit
-- `ademamix_8bit`
-- `lion_8bit`
-- `lion_32bit`
-- `paged_adamw_32bit`
-- `paged_adamw_8bit`
-- `paged_ademamix_32bit`
-- `paged_ademamix_8bit`
-- `paged_lion_32bit`
-- `paged_lion_8bit`
-- `rmsprop`
-- `rmsprop_bnb`
-- `rmsprop_bnb_8bit`
-- `rmsprop_bnb_32bit`
-- `galore_adamw`
-- `galore_adamw_8bit`
-- `galore_adafactor`
-- `galore_adamw_layerwise`
-- `galore_adamw_8bit_layerwise`
-- `galore_adafactor_layerwise`
-- `lomo`
-- `adalomo`
-- `grokadamw`
-- `schedule_free_radam`
-- `schedule_free_adamw`
-- `schedule_free_sgd`
-- `apollo_adamw`
-- `apollo_adamw_layerwise`
-- `stable_adamw`
-
-
-## Custom Optimizers
-
-Enable custom optimizers by passing a string to the `optimizer` argument. Each optimizer will receive beta and epsilon args, however, some may accept additional args which are detailed below.
-
-### optimi_adamw
-
-```yaml
-optimizer: optimi_adamw
-```
-
-### ao_adamw_4bit
-
-Deprecated: Please use `adamw_torch_4bit`.
-
-### ao_adamw_8bit
-
-Deprecated: Please use `adamw_torch_8bit`.
-
-### ao_adamw_fp8
-
-
-```yaml
-optimizer: ao_adamw_fp8
-```
-
-### adopt_adamw
-
-GitHub: [https://github.com/iShohei220/adopt](https://github.com/iShohei220/adopt)
-Paper: [https://arxiv.org/abs/2411.02853](https://arxiv.org/abs/2411.02853)
-
-```yaml
-optimizer: adopt_adamw
-```
-
-### came_pytorch
-
-GitHub: [https://github.com/yangluo7/CAME/tree/master](https://github.com/yangluo7/CAME/tree/master)
-Paper: [https://arxiv.org/abs/2307.02047](https://arxiv.org/abs/2307.02047)
-
-```yaml
-optimizer: came_pytorch
-
-# optional args (defaults below)
-adam_beta1: 0.9
-adam_beta2: 0.999
-adam_beta3: 0.9999
-adam_epsilon: 1e-30
-adam_epsilon2: 1e-16
-```
-
-### muon
-
-Blog: [https://kellerjordan.github.io/posts/muon/](https://kellerjordan.github.io/posts/muon/)
-Paper: [https://arxiv.org/abs/2502.16982v1](https://arxiv.org/abs/2502.16982v1)
-
-```yaml
-optimizer: muon
-```
-
-### dion
-
-Microsoft's Dion (DIstributed OrthoNormalization) optimizer is a scalable and communication-efficient
-orthonormalizing optimizer that uses low-rank approximations to reduce gradient communication.
-
-GitHub: [https://github.com/microsoft/dion](https://github.com/microsoft/dion)
-Paper: [https://arxiv.org/pdf/2504.05295](https://arxiv.org/pdf/2504.05295)
-Note: Implementation written for PyTorch 2.7+ for DTensor
-
-```yaml
-optimizer: dion
-dion_lr: 0.01
-dion_momentum: 0.95
-lr: 0.00001  # learning rate for embeddings and parameters that fallback to AdamW
-```

docs/sequence_parallelism.qmd

@@ -22,7 +22,7 @@ To enable sequence parallelism, add the following to your configuration file:
 
 ```yaml
 # Set to a divisor (> 1) of the number of GPUs available
-context_parallel_size: 4  # Split sequences across 4 GPUs
+sequence_parallel_degree: 4  # Split sequences across 4 GPUs
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
 # Optional; one of "varlen_llama3" or "batch_ring". Defaults to
@@ -30,7 +30,7 @@ heads_k_stride: 1
 ring_attn_func:
 ```
 
-The `context_parallel_size` should be a divisor of the total number of GPUs. For example:
+The `sequence_parallel_degree` should be a divisor of the total number of GPUs. For example:
 
 - With 8 GPUs, valid values would be 2, 4, or 8
 - With 4 GPUs, valid values would be 2 or 4
@@ -66,7 +66,7 @@ sequence_len: 8192
 
 ...
 
-context_parallel_size: 4  # Split each sequence into 4 parts, one per GPU
+sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
 # Optional; one of "varlen_llama3" or "batch_ring". Defaults to
@@ -89,12 +89,12 @@ Sequence parallelism is compatible with Axolotl's sample packing functionality.
 
 ## Effect on Batch Size
 
-When using sequence parallelism, your effective global batch size is **divided** by the `context_parallel_size`. This happens because:
+When using sequence parallelism, your effective global batch size is **divided** by the `sequence_parallel_degree`. This happens because:
 
-- Each group of `context_parallel_size` GPUs works on the same batch (just different parts of each sequence)
+- Each group of `sequence_parallel_degree` GPUs works on the same batch (just different parts of each sequence)
 - The number of batches processed per step decreases
 
 For example:
 - With 8 GPUs and no sequence parallelism: 8 different batches processed per step
-- With 8 GPUs and `context_parallel_size=4`: Only 2 different batches processed per step (each split across 4 GPUs)
+- With 8 GPUs and `sequence_parallel_degree=4`: Only 2 different batches processed per step (each split across 4 GPUs)
 - If your per-GPU `micro_batch_size` is 2, the global batch size decreases from 16 to 4

examples/alst/README.md

@@ -1,9 +0,0 @@
-# Arctic Long Sequence Training (ALST)
-
-Artic Long Sequence Training (ALST) is a technique for training long context models using a variety of optimization
-techniques. It is a combination of:
-- TiledMLP: Leverage tiling over the sequence dimension on MLP layers to reduce memory usage
-- Tiled Loss: Using optimized loss functions like Liger-Kernel or Cut Cross Entropy to reduce memory usage
-- Activation Offloading: Offload activations to CPU RAM to reduce memory usage
-
-For more information, you can check out the ALST paper [here](https://www.arxiv.org/abs/2506.13996).

examples/alst/llama3-8b-deepspeed-alst.yaml

@@ -1,53 +0,0 @@
-base_model: meta-llama/Llama-3.1-8B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-datasets:
-  - path: togethercomputer/Long-Data-Collections
-    type: completion
-    field: text
-    data_files:
-      - pretrain/rp_sub.jsonl.zst
-  - path: princeton-nlp/TextbookChapters
-    type: completion
-    field: chapter
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-sequence_len: 500_000
-min_sample_len: 200_000
-sample_packing: true
-
-tiled_mlp: true
-context_parallel_size: 8
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch_8bit
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-activation_offloading: legacy
-
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 100
-saves_per_epoch: 1
-evals_per_epoch: 2
-weight_decay: 0.0
-special_tokens:
-  pad_token: <|end_of_text|>
-
-deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_all.json
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/alst/llama3-8b-fsdp2-alst.yaml

@@ -1,59 +0,0 @@
-base_model: meta-llama/Llama-3.1-8B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-datasets:
-  - path: togethercomputer/Long-Data-Collections
-    type: completion
-    field: text
-    data_files:
-      - pretrain/rp_sub.jsonl.zst
-  - path: princeton-nlp/TextbookChapters
-    type: completion
-    field: chapter
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-sequence_len: 500_000
-min_sample_len: 200_000
-sample_packing: true
-
-tiled_mlp: true
-context_parallel_size: 8
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch_8bit
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-activation_offloading: legacy
-
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 100
-saves_per_epoch: 1
-evals_per_epoch: 2
-weight_decay: 0.0
-special_tokens:
-  pad_token: <|end_of_text|>
-
-fsdp_version: 2
-fsdp_config:
-  offload_params: false  # offloading is currently not compatible with SP + torchao optimizer
-  state_dict_type: SHARDED_STATE_DICT
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  reshard_after_forward: true
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/arcee/README.md

@@ -1,53 +0,0 @@
-# Finetune ArceeAI's AFM with Axolotl
-
-[Arcee Foundation Models (AFM)](https://huggingface.co/collections/arcee-ai/afm-45b-68823397c351603014963473) are a family of 4.5B parameter open weight models trained by Arcee.ai.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-Thanks to the team at Arcee.ai for using Axolotl in supervised fine-tuning the AFM model.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as AFM is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
-
-    Here is an example of how to install from main for pip:
-
-```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-git clone https://github.com/axolotl-ai-cloud/axolotl.git
-cd axolotl
-
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation -e '.[flash-attn]'
-```
-
-2. Run the finetuning example:
-
-```bash
-axolotl train examples/arcee/afm-4.5b-qlora.yaml
-```
-
-This config uses about 7.8GiB VRAM.
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### TIPS
-
-- For inference, the official Arcee.ai team recommends `top_p: 0.95`, `temperature: 0.5`, `top_k: 50`, and `repeat_penalty: 1.1`.
-- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
-- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
-- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
-
-## Related Resources
-
-- [AFM Blog](https://docs.arcee.ai/arcee-foundation-models/introduction-to-arcee-foundation-models)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/arcee/afm-4.5b-qlora.yaml

@@ -1,64 +0,0 @@
-base_model: arcee-ai/AFM-4.5B
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/archived/cerebras/btlm-ft.yml

@@ -66,7 +66,7 @@ flash_optimum:
 gptq_groupsize:
 gptq_model_v1:
 
-warmup_ratio: 0.1
+warmup_steps: 32
 evals_per_epoch: 4
 saves_per_epoch: 1
 save_total_limit:

examples/archived/cerebras/qlora.yml

@@ -43,7 +43,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/code-llama/13b/lora.yml

@@ -47,7 +47,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/code-llama/13b/qlora.yml

@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/code-llama/34b/lora.yml

@@ -47,7 +47,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/code-llama/34b/qlora.yml

@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/code-llama/7b/lora.yml

@@ -47,7 +47,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/code-llama/7b/qlora.yml

@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/dbrx/16bit-lora.yaml

@@ -54,7 +54,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 

examples/archived/dbrx/8bit-lora.yaml

@@ -57,7 +57,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 

examples/archived/dbrx/fft-ds-zero3.yaml

@@ -41,7 +41,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 

examples/archived/deepcoder/deepcoder-14B-preview-lora.yml

@@ -51,7 +51,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/falcon/config-7b-lora.yml

@@ -47,7 +47,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 40
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/falcon/config-7b-qlora.yml

@@ -77,7 +77,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.000001

examples/archived/falcon/config-7b.yml

@@ -44,7 +44,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 40
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/gptj/qlora.yml

@@ -40,7 +40,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/jeopardy-bot/config.yml

@@ -41,7 +41,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/mpt-7b/config.yml

@@ -42,7 +42,7 @@ logging_steps: 5
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0001

examples/archived/openllama-3b/config.yml

@@ -42,7 +42,7 @@ logging_steps: 1
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/openllama-3b/lora.yml

@@ -50,7 +50,7 @@ logging_steps: 1
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/openllama-3b/qlora.yml

@@ -43,7 +43,7 @@ logging_steps: 1
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/qwen/lora.yml

@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention:
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/qwen/qlora.yml

@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention:
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/qwen/qwen2-moe-lora.yaml

@@ -45,7 +45,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/qwen/qwen2-moe-qlora.yaml

@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/redpajama/config-3b.yml

@@ -43,7 +43,7 @@ logging_steps: 5
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0001

examples/archived/replit-3b/config-lora.yml

@@ -41,7 +41,7 @@ logging_steps: 1
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0

examples/archived/stablelm-2/1.6b/fft.yml

@@ -47,9 +47,10 @@ logging_steps: 1
 flash_attention: true
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
+flash_attn_fuse_qkv: false
 flash_attn_fuse_mlp: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
 

examples/archived/stablelm-2/1.6b/lora.yml

@@ -51,7 +51,7 @@ flash_attention: true
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/starcoder2/qlora.yml

@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 eval_steps:
 saves_per_epoch: 4

examples/archived/tiny-llama/lora-mps.yml

@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: false
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 0
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/tiny-llama/lora.yml

@@ -47,7 +47,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/tiny-llama/pretrain.yml

@@ -38,7 +38,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/tiny-llama/qlora.yml

@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/xgen-7b/xgen-7b-8k-qlora.yml

@@ -75,7 +75,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/yi-34B-chat/qlora.yml

@@ -20,7 +20,7 @@ special_tokens:
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-warmup_ratio: 0.1
+warmup_steps: 10
 
 # Iterations
 num_epochs: 1

examples/colab-notebooks/colab-axolotl-example.ipynb

@@ -40,7 +40,7 @@
         "%%capture\n",
         "# This step can take ~5-10 minutes to install dependencies\n",
         "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
-        "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@0ee9ee8\""
+        "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@631d646\""
       ]
     },
     {

examples/deepcogito/cogito-v1-preview-llama-3B-lora.yml

@@ -51,7 +51,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0

examples/deepcogito/cogito-v1-preview-qwen-14B-lora.yml

@@ -51,7 +51,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -37,7 +37,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 2
 saves_per_epoch: 1
 weight_decay: 0.0

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -61,7 +61,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 2
 saves_per_epoch: 1
 weight_decay: 0.0

examples/devstral/README.md

@@ -10,14 +10,17 @@ Thanks to the team at MistralAI for giving us early access to prepare for this r
 
 ## Getting started
 
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Devstral is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
 
-    Here is an example of how to install from pip:
+    Here is an example of how to install from main for pip:
 
 ```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+# Ensure you have Pytorch installed (Pytorch 2.6.0+)
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+
 pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+pip3 install --no-build-isolation -e '.[flash-attn]'
 ```
 
 2. Run the finetuning example:

examples/distributed-parallel/README.md

@@ -1,52 +0,0 @@
-# ND Parallelism Examples
-
-This directory contains example configurations for training models using ND Parallelism in Axolotl. These examples demonstrate how to compose different parallelism strategies (FSDP, TP, CP, HSDP) for efficient multi-GPU training.
-
-## Quick Start
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Run the command below:
-
-```bash
-# Train Qwen3 8B with FSDP + TP + CP on a single 8-GPU node
-axolotl train examples/distributed-parallel/qwen3-8b-fsdp-tp-cp.yaml
-
-# Train Llama 3.1 8B with HSDP + TP on 2 nodes (16 GPUs total)
-axolotl train examples/distributed-parallel/llama-3_1-8b-hsdp-tp.yaml
-```
-
-## Example Configurations
-
-### Single Node (8 GPUs)
-
-**Qwen3 8B with FSDP + TP + CP** ([qwen3-8b-fsdp-tp-cp.yaml](./qwen3-8b-fsdp-tp-cp.yaml))
-- Uses all 3 parallelism dimensions on a single node
-- Ideal for: when model weights, activations, and/or context are too large to fit on single GPU
-
-```yaml
-dp_shard_size: 2         # FSDP across 2 GPUs
-tensor_parallel_size: 2  # TP across 2 GPUs
-context_parallel_size: 2 # CP across 2 GPUs
-# Total: 2 × 2 × 2 = 8 GPUs
-```
-
-### Multi-Node
-
-**Llama 3.1 8B with HSDP + TP** ([llama-3_1-8b-hsdp-tp.yaml](./llama-3_1-8b-hsdp-tp.yaml))
-- FSDP & TP within nodes, DDP across nodes to minimize inter-node communication
-- Ideal for: Scaling to multiple nodes while maintaining training efficiency
-
-```yaml
-dp_shard_size: 4        # FSDP within each 4-GPU group
-tensor_parallel_size: 2 # TP within each node
-dp_replicate_size: 2    # DDP across 2 groups
-# Total: (4 × 2) × 2 = 16 GPUs (2 nodes)
-```
-
-## Learn More
-
-- [ND Parallelism Documentation](https://docs.axolotl.ai/docs/nd_parallelism.html)
-- [Blog: Accelerate ND-Parallel Guide](https://huggingface.co/blog/accelerate-nd-parallel)
-- [Multi-GPU Training Guide](https://docs.axolotl.ai/docs/multi-gpu.html)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/distributed-parallel/llama-3_1-8b-hsdp-tp.yaml

@@ -1,47 +0,0 @@
-base_model: meta-llama/Llama-3.1-8B
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-dp_shard_size: 4
-dp_replicate_size: 2
-tensor_parallel_size: 2
-# context_parallel_size: 2
-
-dataset_prepared_path: last_run_prepared
-
-special_tokens:
-  pad_token: <|end_of_text|>
-
-fsdp_version: 2
-fsdp_config:
-  offload_params: false
-  state_dict_type: FULL_STATE_DICT
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  reshard_after_forward: true
-
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-
-output_dir: ./outputs/ndp-out/
-
-sequence_len: 2048
-sample_packing: true
-flash_attention: true
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 2
-optimizer: adamw_torch_fused
-lr_scheduler: constant_with_warmup
-learning_rate: 2e-6
-
-bf16: true
-tf32: true
-
-logging_steps: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1

examples/distributed-parallel/qwen3-8b-fsdp-tp-cp.yaml

@@ -1,46 +0,0 @@
-base_model: Qwen/Qwen3-8B
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-dp_shard_size: 2
-# dp_replicate_size: 1
-context_parallel_size: 2
-tensor_parallel_size: 2
-
-dataset_prepared_path: last_run_prepared
-
-fsdp_version: 2
-fsdp_config:
-  offload_params: false
-  state_dict_type: FULL_STATE_DICT
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Qwen3DecoderLayer
-  reshard_after_forward: true
-
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-
-output_dir: ./outputs/ndp-out/
-
-sequence_len: 8192
-sample_packing: true
-flash_attention: true
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1  # must be 1 when using context parallel
-num_epochs: 2
-optimizer: adamw_torch_fused
-lr_scheduler: constant_with_warmup
-learning_rate: 2e-6
-
-bf16: true
-tf32: true
-
-logging_steps: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-
-special_tokens:

examples/gemma3n/README.md

@@ -1,62 +1,19 @@
-# Finetune Gemma-3n with Axolotl
+# Gemma-3n
 
-Gemma-3n is a family of multimodal models from Google found on [HuggingFace](https://huggingface.co/collections/google/gemma-3n-685065323f5984ef315c93f4). This guide shows how to fine-tune it with Axolotl.
+## Requirements
 
-## Getting started
+In addition to Axolotl's requirements, Gemma-3n requires
 
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+```
-
+pip3 install timm
-    Here is an example of how to install from pip:
-
-```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 
-2. In addition to Axolotl's requirements, Gemma-3n requires:
+If you will load audio datasets, please also install
 
-```bash
+```
-pip3 install timm==1.0.17
+pip3 install librosa
-
-# for loading audio data
-pip3 install librosa==0.11.0
 ```
 
-3. Run the finetuning example:
+## Usage
 
-```bash
+See example configs and the [multimodal doc](https://docs.axolotl.ai/docs/multimodal.html).
-# text only
-axolotl train examples/gemma3n/gemma-3n-e2b-qlora.yml
-
-# text + vision
-axolotl train examples/gemma3n/gemma-3n-e2b-vision-qlora.yml
-
-# text + vision + audio
-axolotl train examples/gemma3n/gemma-3n-e2b-vision-audio-qlora.yml
-```
-
-Let us know how it goes. Happy finetuning! 🚀
-
-WARNING: The loss and grad norm will be much higher than normal. We suspect this to be inherent to the model as of the moment. If anyone would like to submit a fix for this, we are happy to take a look.
-
-### TIPS
-
-- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-- The text dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-- The multimodal dataset format follows the OpenAI multi-content Messages format as seen [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
-
-## Optimization Guides
-
-- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
-- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
-- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
-
-## Related Resources
-
-- [Gemma 3n Blog](https://ai.google.dev/gemma/docs/gemma-3n)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/gemma3n/gemma-3n-e2b-vision-audio-qlora.yml

@@ -34,6 +34,8 @@ eot_tokens:
 datasets:
   - path: Nanobit/text-vision-audio-2k-test
     type: chat_template
+    data_files:
+      - dataset.jsonl
 dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./outputs/out

examples/glm4/qlora-32b.yaml

@@ -55,7 +55,7 @@ flash_attention: true
 loss_watchdog_threshold: 5.0
 loss_watchdog_patience: 3
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0

examples/gpt-oss/README.md

@@ -1,74 +0,0 @@
-# Finetune OpenAI's GPT-OSS with Axolotl
-
-[GPT-OSS](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) are a family of open-weight MoE models trained by OpenAI, released in August 2025. There are two variants: 20B and 120B.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## 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 Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
-```
-
-2. Choose one of the following configs below for training the 20B model. (for 120B, see [below](#training-120b))
-
-```bash
-# LoRA SFT linear layers (1x48GB @ ~44GiB)
-axolotl train examples/gpt-oss/gpt-oss-20b-sft-lora-singlegpu.yaml
-
-# FFT SFT with offloading (2x24GB @ ~21GiB/GPU)
-axolotl train examples/gpt-oss/gpt-oss-20b-fft-fsdp2-offload.yaml
-
-# FFT SFT (8x48GB @ ~36GiB/GPU or 4x80GB @ ~46GiB/GPU)
-axolotl train examples/gpt-oss/gpt-oss-20b-fft-fsdp2.yaml
-```
-
-Note: Memory usage taken from `device_mem_reserved(gib)` from logs.
-
-### Training 120B
-
-On 8xH100s
-
-```bash
-# FFT SFT with offloading (8x80GB @ ~49GiB/GPU)
-axolotl train examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml
-```
-
-### Tool use
-
-GPT-OSS has a comprehensive tool understanding. Axolotl supports tool calling datasets for Supervised Fine-tuning.
-
-Here is an example dataset config:
-```yaml
-datasets:
-  - path: Nanobit/text-tools-2k-test
-    type: chat_template
-```
-
-See [Nanobit/text-tools-2k-test](https://huggingface.co/datasets/Nanobit/text-tools-2k-test) for the sample dataset.
-
-Refer to [our docs](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#using-tool-use) for more info.
-
-### TIPS
-
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
-- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
-
-## Related Resources
-
-- [GPT-OSS Blog](https://openai.com/index/introducing-gpt-oss/)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

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

@@ -1,67 +0,0 @@
-# the original mxfp4 quantized model is not supported with FSDP cpu_ram_efficient_loading
-# FSDP cpu_ram_efficient_loading is used to reduce the initial CPU memory usage when loading the model
-base_model: axolotl-ai-co/gpt-oss-120b-dequantized
-
-use_kernels: false
-
-dp_shard_size: 16  # requires 2x8xH100 nodes
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding
-
-datasets:
-  - path: HuggingFaceH4/Multilingual-Thinking
-    type: chat_template
-    field_thinking: thinking
-    template_thinking_key: thinking
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./outputs/gpt-oss-out/
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 1
-num_epochs: 1
-
-optimizer: adamw_torch_fused  # 8bit optimizers do not work with FSDP2 offload
-lr_scheduler: constant_with_warmup
-learning_rate: 2e-5
-
-bf16: true
-tf32: true
-
-flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
-
-gradient_checkpointing: true
-activation_offloading: true
-
-logging_steps: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.03
-
-special_tokens:
-eot_tokens:
-  - "<|end|>"
-
-fsdp_version: 2
-fsdp_config:
-  offload_params: true
-  state_dict_type: SHARDED_STATE_DICT
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: GptOssDecoderLayer
-  reshard_after_forward: true
-  cpu_ram_efficient_loading: true

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

@@ -1,58 +0,0 @@
-base_model: openai/gpt-oss-20b
-use_kernels: false
-model_quantization_config: Mxfp4Config
-model_quantization_config_kwargs:
-  dequantize: true
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding
-
-datasets:
-  - path: HuggingFaceH4/Multilingual-Thinking
-    type: chat_template
-    field_thinking: thinking
-    template_thinking_key: thinking
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./outputs/gpt-oss-out/
-
-sequence_len: 4096
-sample_packing: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 1
-num_epochs: 1
-
-optimizer: adamw_torch_8bit
-lr_scheduler: constant_with_warmup
-learning_rate: 2e-5
-
-bf16: true
-tf32: true
-
-flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
-
-gradient_checkpointing: true
-activation_offloading: true
-
-logging_steps: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.03
-
-special_tokens:
-eot_tokens:
-  - "<|end|>"
-
-# choose the zero3 configuration that best fits your system capabilities
-deepspeed: deepspeed_configs/zero3_bf16.json

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

@@ -1,68 +0,0 @@
-base_model: openai/gpt-oss-20b
-use_kernels: true
-model_quantization_config: Mxfp4Config
-model_quantization_config_kwargs:
-  dequantize: true
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding
-
-datasets:
-  - path: HuggingFaceH4/Multilingual-Thinking
-    type: chat_template
-    field_thinking: thinking
-    template_thinking_key: thinking
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./outputs/gpt-oss-out/
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 1
-num_epochs: 1
-
-optimizer: adamw_torch_fused  # 8bit optimizers do not work with FSDP2 offload
-lr_scheduler: constant_with_warmup
-learning_rate: 2e-5
-
-bf16: true
-tf32: true
-
-flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
-
-gradient_checkpointing: true
-activation_offloading: true
-
-logging_steps: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.03
-
-special_tokens:
-eot_tokens:
-  - "<|end|>"
-
-fsdp_version: 2
-fsdp_config:
-  offload_params: true
-  state_dict_type: SHARDED_STATE_DICT
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: GptOssDecoderLayer
-  reshard_after_forward: true
-  #  cpu_ram_efficient_loading: true
-
-# cpu_ram_efficient_loading cannot be used with MXFP4 model quantization.
-# It can only be used with a dequantized model like `axolotl-ai-co/gpt-oss-120b-dequantized`

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

@@ -1,64 +0,0 @@
-base_model: openai/gpt-oss-20b
-use_kernels: false
-model_quantization_config: Mxfp4Config
-model_quantization_config_kwargs:
-  dequantize: true
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding
-
-datasets:
-  - path: HuggingFaceH4/Multilingual-Thinking
-    type: chat_template
-    field_thinking: thinking
-    template_thinking_key: thinking
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./outputs/gpt-oss-out/
-
-sequence_len: 4096
-sample_packing: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 1
-num_epochs: 1
-
-optimizer: adamw_torch_8bit
-lr_scheduler: constant_with_warmup
-learning_rate: 2e-5
-
-bf16: true
-tf32: true
-
-flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
-
-gradient_checkpointing: true
-activation_offloading: true
-
-logging_steps: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.03
-
-special_tokens:
-eot_tokens:
-  - "<|end|>"
-
-fsdp_version: 2
-fsdp_config:
-  offload_params: false
-  state_dict_type: SHARDED_STATE_DICT
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: GptOssDecoderLayer
-  reshard_after_forward: true
-#  cpu_ram_efficient_loading: true

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

@@ -1,67 +0,0 @@
-base_model: openai/gpt-oss-20b
-use_kernels: true
-model_quantization_config: Mxfp4Config
-model_quantization_config_kwargs:
-  dequantize: true
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-experimental_skip_move_to_device: true  # prevent OOM by not putting model to GPU before sharding
-
-datasets:
-  - path: HuggingFaceH4/Multilingual-Thinking
-    type: chat_template
-    field_thinking: thinking
-    template_thinking_key: thinking
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./outputs/gpt-oss-out/
-
-sequence_len: 4096
-sample_packing: true
-
-adapter: lora
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.0  # dropout not supported when using LoRA over expert parameters
-lora_target_linear: true
-
-# TODO: not supported for now, see peft#2710
-#lora_target_parameters:  # target the experts in the last two layers
-#  - "22._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
-#  - "22._checkpoint_wrapped_module.mlp.experts.down_proj"
-#  - "23._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
-#  - "23._checkpoint_wrapped_module.mlp.experts.down_proj"
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 8
-micro_batch_size: 1
-num_epochs: 1
-
-optimizer: adamw_torch_8bit
-lr_scheduler: constant_with_warmup
-learning_rate: 2e-4
-
-bf16: true
-tf32: true
-
-flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
-
-gradient_checkpointing: true
-activation_offloading: true
-
-logging_steps: 1
-saves_per_epoch: 1
-warmup_ratio: 0.1
-
-special_tokens:
-eot_tokens:
-  - "<|end|>"

examples/jamba/qlora.yaml

@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 weight_decay: 0.0

examples/jamba/qlora_deepspeed.yaml

@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 

examples/jamba/qlora_fsdp_large.yaml

@@ -47,7 +47,7 @@ gradient_checkpointing_kwargs:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0

examples/llama-2/fft_optimized.yml

@@ -45,9 +45,10 @@ logging_steps: 1
 flash_attention: true
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
+flash_attn_fuse_qkv: false
 flash_attn_fuse_mlp: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
 

examples/llama-2/gptq-lora.yml

@@ -56,7 +56,7 @@ logging_steps: 1
 flash_attention:
 sdp_attention:
 flash_optimum:
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/llama-2/lisa.yml

@@ -49,9 +49,10 @@ logging_steps: 1
 flash_attention: true
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
+flash_attn_fuse_qkv: false
 flash_attn_fuse_mlp: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/llama-2/loftq.yml

@@ -47,7 +47,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/llama-2/lora.yml

@@ -47,7 +47,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/llama-2/qlora-fsdp.yml

@@ -50,7 +50,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/llama-2/qlora.yml

@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/llama-2/relora.yml

@@ -25,12 +25,9 @@ lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
 
-relora: true
+relora_steps: 150
-relora_prune_ratio: 0.9
+relora_warmup_steps: 10
 relora_cpu_offload: false
-jagged_restart_steps: 150
-jagged_restart_warmup_steps: 10
-jagged_restart_anneal_steps: false
 
 wandb_project:
 wandb_entity:
@@ -53,7 +50,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/llama-3/3b-qat-fsdp2.yaml

@@ -58,7 +58,7 @@ logging_steps: 1
 evals_per_epoch: 1
 saves_per_epoch: 1
 
-warmup_ratio: 0.1
+warmup_steps: 10
 weight_decay: 0.0
 fsdp:
   - full_shard

examples/llama-3/fft-8b-liger-fsdp.yaml

@@ -51,7 +51,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 2
 saves_per_epoch: 1
 weight_decay: 0.0

examples/llama-3/fft-8b.yaml

@@ -36,7 +36,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 2
 saves_per_epoch: 1
 weight_decay: 0.0

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -67,7 +67,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0