# GPT-OSS

GPT-OSS are a family of open-weight MoE models trained by OpenAI, released in August 2025. There are two variants: 20B and 120B.

In October 2025, OpenAI released safeguard models built upon GPT-OSS called GPT-OSS-Safeguard. They use the same architecture, so the same examples below can be re-used.

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.

   Here is an example of how to install from pip:

    # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
    pip3 install packaging==26.0 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)

    # 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, 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.

    # 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 to showcase multi-node training of the 120B model using Baseten Truss. You can read more about this recipe on Baseten’s blog. The recipe can be found on their GitHub.

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.

    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`.

    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/

### How to set reasoning_effort in template?

The harmony template has a feature to set the `reasoning_effort` during prompt building. The default is `medium`. If you would like to adjust this, you can add the following to your config:

    chat_template_kwargs:
      reasoning_effort: "high"  # low | medium | high

Currently, this applies globally. There is no method to apply per sample yet. If you are interested in adding this, please feel free to create an Issue to discuss.

### 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:

    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):

    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:

    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.

Here is an example dataset config:

    datasets:
      - path: Nanobit/text-tools-2k-test
        type: chat_template

See Nanobit/text-tools-2k-test for the sample dataset.

Refer to our docs for more info.

### Thinking and chat_template masking conflict

OpenAI’s Harmony template hides `thinking` in all non-final turns, which conflicts with Axolotl’s `chat_template` masking.

If your dataset has `thinking` content mid-turn, there are two paths we recommend:

- Train only on the last turn. This can be accomplished via chat_template’s train on last doc.

- Adjust your dataset to only have `thinking` content in the last turn.

### TIPS

- Read more on how to load your own dataset at docs.
- The dataset format follows the OpenAI Messages format as seen here.

## Optimization Guides

- Multi-GPU Training
- Multi-Node Training

## Related Resources

- GPT-OSS Blog
- Axolotl Docs
- Axolotl Website
- Axolotl GitHub
- Axolotl Discord