axolotl/examples/gpt-oss

Finetune OpenAI's GPT-OSS with Axolotl

GPT-OSS 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.

   Here is an example of how to install from pip:

# 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)

# 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/

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