Instructions to use linglingdan/DRIFT-8B-ToolUse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use linglingdan/DRIFT-8B-ToolUse with Transformers:

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

pipe = pipeline("text-generation", model="linglingdan/DRIFT-8B-ToolUse")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("linglingdan/DRIFT-8B-ToolUse")
model = AutoModelForCausalLM.from_pretrained("linglingdan/DRIFT-8B-ToolUse")
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 Settings

vLLM

How to use linglingdan/DRIFT-8B-ToolUse with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "linglingdan/DRIFT-8B-ToolUse"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "linglingdan/DRIFT-8B-ToolUse",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/linglingdan/DRIFT-8B-ToolUse

SGLang

How to use linglingdan/DRIFT-8B-ToolUse 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 "linglingdan/DRIFT-8B-ToolUse" \
    --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": "linglingdan/DRIFT-8B-ToolUse",
		"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 "linglingdan/DRIFT-8B-ToolUse" \
        --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": "linglingdan/DRIFT-8B-ToolUse",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use linglingdan/DRIFT-8B-ToolUse with Docker Model Runner:
```
docker model run hf.co/linglingdan/DRIFT-8B-ToolUse
```

DRIFT-8B-ToolUse / README.md

linglingdan

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	---
	license: other
	base_model: Qwen/Qwen3-8B
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- qwen3
	- causal-lm
	- transformers
	language:
	- zh
	- en
	arxiv: 2606.30345
	github: https://github.com/LianjiaTech/drift
	blog: https://lianjiatech.github.io/drift/blog/
	---

	# Drift-8B-ToolUse

	<!-- 顶部快速跳转徽标 -->
	[![github](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/LianjiaTech/drift)
	[![blog](https://img.shields.io/badge/Blog-Post-orange?logo=blogger)](https://lianjiatech.github.io/drift/blog/)
	[![arxiv](https://img.shields.io/badge/arXiv-2606.30345-b31b1b.svg?logo=arxiv)](https://arxiv.org/abs/2606.30345)

	This repository contains a merged HuggingFace checkpoint fine-tuned based on `Qwen/Qwen3-8B`.

	## Quick Links

	- GitHub Repository: [LianjiaTech/drift](https://github.com/LianjiaTech/drift)
	- Technical Blog: [DRIFT Blog](https://lianjiatech.github.io/drift/blog/)
	- Academic Paper: [arXiv:2606.30345](https://arxiv.org/abs/2606.30345)

	## Model Summary

	- Base model: `Qwen/Qwen3-8B`
	- Architecture: `Qwen3ForCausalLM`
	- Precision: `bfloat16`
	- Context length (config): `max_position_embeddings = 40960`
	- Weights format: sharded `safetensors` (4 shards)

	## Usage

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "Yiwei6534/Drift-8B-ToolUse"

	tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	trust_remote_code=True,
	)

	messages = [
	{"role": "system", "content": "You are a helpful assistant."},
	{"role": "user", "content": "What can you help me with?"},
	]

	text = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	)

	inputs = tokenizer(text, return_tensors="pt").to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=256)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	## Generation Defaults

	The bundled `generation_config.json` uses `temperature=0.6`, `top_k=20`, `top_p=0.95`. Adjust based on your deployment.

	## Integrity Files

	- `FILE_MANIFEST.json`: list of distributed files and their byte sizes.
	- `SHA256SUMS.txt`: SHA256 checksums for all distributed files (verify with `sha256sum -c SHA256SUMS.txt`).

	## Limitations

	- The model may hallucinate tool calls or produce invalid arguments.
	- Output quality depends on the serving template and tool schema formatting.
	- Safety, bias, and domain-specific failure modes are not fully documented here.

	## Citation

	If you find DRIFT or this model helpful in your research, please cite:

	```bibtex
	@article{luo2026drift,
	title={DRIFT: Difficulty Routing Self-DIstillation with Rhythm-Gated Exploration and Success BuFfer Training},
	author={Luo, Haisen and Liu, Yiwei and Wang, Haoning and Liu, Dan and Yin, Junxi and Wang, Haotian and Zhang, Lei and Tian, Xiaoyu and Chen, Shuaiting and Song, Yuansheng and others},
	journal={arXiv preprint arXiv:2606.30345},
	year={2026}
	}
	```

	## License

	This repository uses `license: other` as a placeholder. Replace it with the correct license for the base model, your fine-tuning data, and your distribution terms before publishing.