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README.md

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+---
+base_model: openai/gpt-oss-20b
+library_name: transformers
+tags:
+- lora
+- sft
+- tool-use
+- gpt-oss
+license: apache-2.0
+---
+
+# gpt-oss-claude-code
+
+Fine-tuned [`openai/gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) for tool-use and agentic coding tasks. LoRA adapters merged into base weights.
+
+## Quick start
+```python
+import re, torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "deburky/gpt-oss-claude-code",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("deburky/gpt-oss-claude-code")
+
+messages = [{"role": "user", "content": "Who is Alan Turing?"}]
+inputs = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True,
+    return_tensors="pt", return_dict=True,
+).to(model.device)
+
+with torch.no_grad():
+    out = model.generate(**inputs, max_new_tokens=256)
+response = tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:])
+
+if "<|channel|>final<|message|>" in response:
+    response = response.split("<|channel|>final<|message|>")[-1]
+print(re.sub(r"<\\|[^>]+\\|>", "", response).strip())
+```
+
+## Apple Silicon (MLX)
+
+A fused MLX version is available at [`deburky/gpt-oss-claude-mlx`](https://huggingface.co/deburky/gpt-oss-claude-mlx).
+
+## Training
+
+- **Data:** ~280 tool-use conversation examples in gpt-oss harmony format
+- **Method:** LoRA (rank 8, alpha 16) on attention + MoE expert layers, merged after training
+- **LR:** 1e-4, cosine schedule
+- **Final val loss:** ~0.48
+- **Hardware:** Google Colab