ca796fb56e
* feat(doc): update gpt-oss readme * fix: caps * feat: add toolcalling section * feat: add example tool dataset to docs * chore: update
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
-
Install Axolotl following the installation guide. You need to install from main as GPT-OSS is only on nightly or use our latest Docker images.
Here is an example of how to install from main for pip:
# 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]'
- Choose one of the following configs below for training the 20B model.
# LoRA SFT linear layers & 2 experts (1x48GB @ ~47GiB)
# (only linear layers @ ~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
Notes:
- 120B coming soon!
- Memory usage taken from
device_mem_reserved(gib)from logs.
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.
TIPS
- Read more on how to load your own dataset at docs.
- The dataset format follows the OpenAI Messages format as seen here.