初始化项目，由ModelHub XC社区提供模型

Model: Huggggooo/ProtoCycle-7B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Huggggooo/ProtoCycle-7B-SFT
+tags:
+  - protein-design
+  - agentic
+  - tool-use
+  - qwen2.5
+  - reinforcement-learning
+  - grpo
+language:
+  - en
+---
+
+# ProtoCycle-7B
+
+RL checkpoint for **ProtoCycle** — an agentic protein design model that
+performs multi-step, tool-augmented sequence design.
+
+This is the **GRPO-TCR (Group Relative Policy Optimization with Tool-Call
+Reward) stage**, initialised from the SFT checkpoint
+[`Huggggooo/ProtoCycle-7B-SFT`](https://huggingface.co/Huggggooo/ProtoCycle-7B-SFT).
+
+- Base model: `Huggggooo/ProtoCycle-7B-SFT`
+  (itself fine-tuned from `Qwen/Qwen2.5-7B-Instruct`)
+- Training framework: [VeRL](https://github.com/volcengine/verl) /
+  [Open-AgentRL](https://github.com/Gen-Verse/Open-AgentRL)
+- Stage: agentic RL with GRPO-TCR
+- Rollouts per prompt: 8, max turns: 16
+- Max prompt / response: 8k / 20k tokens
+- Reward manager: `protein` (see
+  [ProtoCycle/verl/workers/reward_manager/protein.py](https://github.com/huggggoooooo/ProtoCycle/blob/main/verl/workers/reward_manager/protein.py))
+
+
+See
+[`recipe/protein/reward.py`](https://github.com/huggggoooooo/ProtoCycle/blob/main/recipe/protein/reward.py)
+for the exact formulation.
+
+## Training Data
+
+10,000 RL prompts for GRPO-TCR training, available at
+[Huggggooo/ProtoCycle-Data](https://huggingface.co/datasets/Huggggooo/ProtoCycle-Data) (`rl/` subset).}
+
+## Agent Protocol
+
+```
+<think>  ... reasoning ...  </think>
+<plan>   ... stage plan ...  </plan>
+<tool_call>{"name": "...", "arguments": {...}}</tool_call>
+...
+<answer>MAEGEITPLKTF...</answer>
+```
+
+## How to Use
+
+See the ProtoCycle repository: 
+[ProtoCycle](https://github.com/huggggoooooo/ProtoCycle) repo.
+
+
+## License
+
+Apache-2.0.
+
+## Citation
+
+If you find this work useful, please cite ProtoCycle (forthcoming) and the
+upstream frameworks: VeRL, Open-AgentRL, ProTrek, ESM.