ecbe8b2b61
* improve fsdp shard merging * improve logging * update information on merging and inferencing GPT-OSS * cleanup readme * automate cleanup of FSDP prefix * import GRPO only if necessary * only modify config.json on rank0 * merge final checkpoint at end of training * prevent circular import * Fix saving for sharded state dict * devx, move merged to output dir * move import back to top * Fix stuck merge * fix conditionals from pr feedback and add test
106 lines
4.2 KiB
Markdown
106 lines
4.2 KiB
Markdown
# Finetune OpenAI's GPT-OSS with Axolotl
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[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.
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This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
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## Getting started
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1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
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Here is an example of how to install from pip:
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```bash
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# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
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pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
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pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
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```
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2. Choose one of the following configs below for training the 20B model. (for 120B, see [below](#training-120b))
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```bash
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# LoRA SFT linear layers (1x48GB @ ~44GiB)
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axolotl train examples/gpt-oss/gpt-oss-20b-sft-lora-singlegpu.yaml
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# FFT SFT with offloading (2x24GB @ ~21GiB/GPU)
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axolotl train examples/gpt-oss/gpt-oss-20b-fft-fsdp2-offload.yaml
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# FFT SFT (8x48GB @ ~36GiB/GPU or 4x80GB @ ~46GiB/GPU)
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axolotl train examples/gpt-oss/gpt-oss-20b-fft-fsdp2.yaml
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```
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Note: Memory usage taken from `device_mem_reserved(gib)` from logs.
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### Training 120B
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On 8xH100s, make sure you have ~3TB of free disk space. With each checkpoint clocking in at ~720GB, along with the base
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model, and final model output, you may need at least 3TB of free disk space to keep at least 2 checkpoints.
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```bash
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# FFT SFT with offloading (8x80GB @ ~49GiB/GPU)
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axolotl train examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml
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```
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ERRATA: Transformers saves the model Architecture prefixed with `FSDP` which needs to be manually renamed in `config.json`.
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See https://github.com/huggingface/transformers/pull/40207 for the status of this issue.
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```bash
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sed -i 's/FSDPGptOssForCausalLM/GptOssForCausalLM/g' ./outputs/gpt-oss-out/config.json
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```
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When using SHARDED_STATE_DICT with FSDP, the final checkpoint should automatically merge the sharded weights to your
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configured `output_dir`. However, if that step fails due to a disk space error, you can take an additional step to
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merge the sharded weights. This step will automatically determine the last checkpoint directory and merge the sharded
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weights to `{output_dir}/merged`.
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```bash
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axolotl merge-sharded-fsdp-weights examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml
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mv ./outputs/gpt-oss-out/merged/* ./outputs/gpt-oss-out/
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```
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### Inferencing your fine-tuned model
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GPT-OSS support in vLLM does not exist in a stable release yet. See https://x.com/MaziyarPanahi/status/1955741905515323425
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for more information about using a special vllm-openai docker image for inferencing with vLLM.
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SGLang has 0-day support in main, see https://github.com/sgl-project/sglang/issues/8833 for infomation on installing
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SGLang from source. Once you've installed SGLang, run the following command to launch a SGLang server:
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```bash
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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
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```
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### Tool use
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GPT-OSS has a comprehensive tool understanding. Axolotl supports tool calling datasets for Supervised Fine-tuning.
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Here is an example dataset config:
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```yaml
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datasets:
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- path: Nanobit/text-tools-2k-test
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type: chat_template
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```
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See [Nanobit/text-tools-2k-test](https://huggingface.co/datasets/Nanobit/text-tools-2k-test) for the sample dataset.
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Refer to [our docs](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#using-tool-use) for more info.
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### TIPS
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- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
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- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
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## Optimization Guides
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- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
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- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
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## Related Resources
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- [GPT-OSS Blog](https://openai.com/index/introducing-gpt-oss/)
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- [Axolotl Docs](https://docs.axolotl.ai)
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- [Axolotl Website](https://axolotl.ai)
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- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
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- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
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