初始化项目，由ModelHub XC社区提供模型

Model: linglingdan/DRIFT-8B-Chemistry
Source: Original Platform

README.md

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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-Chemistry
+
+<!-- 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-Chemistry"
+
+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.