103 lines
3.2 KiB
Markdown
103 lines
3.2 KiB
Markdown
---
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license: other
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base_model: Qwen/Qwen3-8B
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- qwen3
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- causal-lm
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- transformers
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language:
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- zh
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- en
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arxiv: 2606.30345
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github: https://github.com/LianjiaTech/drift
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blog: https://lianjiatech.github.io/drift/blog/
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---
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# Drift-8B-Material
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<!-- 顶部快速跳转徽标 -->
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[](https://github.com/LianjiaTech/drift)
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[](https://lianjiatech.github.io/drift/blog/)
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[](https://arxiv.org/abs/2606.30345)
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This repository contains a merged HuggingFace checkpoint fine-tuned based on `Qwen/Qwen3-8B`.
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## Quick Links
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- **GitHub Repository**: [LianjiaTech/drift](https://github.com/LianjiaTech/drift)
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- **Technical Blog**: [DRIFT Blog](https://lianjiatech.github.io/drift/blog/)
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- **Academic Paper**: [arXiv:2606.30345](https://arxiv.org/abs/2606.30345)
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## Model Summary
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- Base model: `Qwen/Qwen3-8B`
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- Architecture: `Qwen3ForCausalLM`
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- Precision: `bfloat16`
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- Context length (config): `max_position_embeddings = 40960`
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- Weights format: sharded `safetensors` (4 shards)
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Yiwei6534/Drift-8B-Material"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "What can you help me with?"},
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Generation Defaults
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The bundled `generation_config.json` uses `temperature=0.6`, `top_k=20`, `top_p=0.95`. Adjust based on your deployment.
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## Integrity Files
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- `FILE_MANIFEST.json`: list of distributed files and their byte sizes.
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- `SHA256SUMS.txt`: SHA256 checksums for all distributed files (verify with `sha256sum -c SHA256SUMS.txt`).
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## Limitations
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- The model may hallucinate tool calls or produce invalid arguments.
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- Output quality depends on the serving template and tool schema formatting.
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- Safety, bias, and domain-specific failure modes are not fully documented here.
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## Citation
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If you find DRIFT or this model helpful in your research, please cite:
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```bibtex
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@article{luo2026drift,
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title={DRIFT: Difficulty Routing Self-DIstillation with Rhythm-Gated Exploration and Success BuFfer Training},
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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},
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journal={arXiv preprint arXiv:2606.30345},
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year={2026}
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}
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```
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## License
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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. |