Instructions to use deburky/gpt-oss-claude-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deburky/gpt-oss-claude-code with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="deburky/gpt-oss-claude-code")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deburky/gpt-oss-claude-code")
model = AutoModelForCausalLM.from_pretrained("deburky/gpt-oss-claude-code")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use deburky/gpt-oss-claude-code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deburky/gpt-oss-claude-code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deburky/gpt-oss-claude-code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deburky/gpt-oss-claude-code

SGLang

How to use deburky/gpt-oss-claude-code with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "deburky/gpt-oss-claude-code" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deburky/gpt-oss-claude-code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "deburky/gpt-oss-claude-code" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deburky/gpt-oss-claude-code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deburky/gpt-oss-claude-code with Docker Model Runner:
```
docker model run hf.co/deburky/gpt-oss-claude-code
```

gpt-oss-claude-code / 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