feat(doc): update gpt-oss readme

docs/dataset-formats/conversation.qmd

@@ -212,10 +212,11 @@ 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: ...
+  - path: Nanobit/text-tools-2k-test
     type: chat_template
     # field_tools: tools # default is `tools`
 ```

examples/gpt-oss/README.md

@@ -1,9 +1,71 @@
-# OpenAI's GPT-OSS
+# Finetune OpenAI's GPT-OSS with Axolotl
 
-GPT-OSS is a 20 billion parameter MoE model trained by OpenAI, released in August 2025.
+[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.
 
-- 20B Full Parameter SFT can be trained on 8x48GB GPUs (peak reserved memory @ ~36GiB/GPU) - [YAML](./gpt-oss-20b-fft-fsdp2.yaml)
-- 20B LoRA SFT (all linear layers, and experts in last two layers) can be trained a single GPU (peak reserved memory @ ~47GiB)
-  - removing the experts from `lora_target_parameters` will allow the model to fit around ~44GiB of VRAM
-  - [YAML](./gpt-oss-20b-sft-lora-singlegpu.yaml)
-- 20B Full Parameter SFT with FSDP2 offloading can be trained on 2x24GB GPUs (peak reserved memory @ ~21GiB/GPU) - [YAML](./gpt-oss-20b-fft-fsdp2-offload.yaml)
+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). You need to install from main as GPT-OSS 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. Choose one of the following configs below for training the 20B model.
+
+```bash
+# 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:
+```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)