Instructions to use taki555/Qwen3-0.6B-Timber with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use taki555/Qwen3-0.6B-Timber with Transformers:

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

pipe = pipeline("text-generation", model="taki555/Qwen3-0.6B-Timber")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("taki555/Qwen3-0.6B-Timber")
model = AutoModelForCausalLM.from_pretrained("taki555/Qwen3-0.6B-Timber")
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 taki555/Qwen3-0.6B-Timber with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "taki555/Qwen3-0.6B-Timber"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "taki555/Qwen3-0.6B-Timber",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/taki555/Qwen3-0.6B-Timber

SGLang

How to use taki555/Qwen3-0.6B-Timber 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 "taki555/Qwen3-0.6B-Timber" \
    --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": "taki555/Qwen3-0.6B-Timber",
		"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 "taki555/Qwen3-0.6B-Timber" \
        --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": "taki555/Qwen3-0.6B-Timber",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use taki555/Qwen3-0.6B-Timber with Docker Model Runner:
```
docker model run hf.co/taki555/Qwen3-0.6B-Timber
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-0.6B-Timber

Official weights for paper Timber: Training-free Instruct Model Refining with Base via Effective Rank

[🤗 HF Models] • [📜 Paper] • [🐱 GitHub]

Please check our paper for more details.

☕️ Citation

If you find this repository helpful, please consider citing our paper:

@article{wu2025timber,
  title={Timber: Training-free Instruct Model Refining with Base via Effective Rank},
  author={Wu, Taiqiang and Yang, Runming and Liu, Tao and Wang, Jiahao and Xu, Zenan and Wong, Ngai.},
  journal={arXiv preprint arXiv:2509.23595},
  year={2025}
}

For any questions, please pull an issue or email at takiwu@connect.hku.hk