初始化项目，由ModelHub XC社区提供模型

Model: McClain/PlasmidGPT-RL
Source: Original Platform

README.md

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+---
+base_model: UCL-CSSB/PlasmidGPT-SFT
+library_name: transformers
+model_name: PlasmidGPT-RL
+tags:
+- generated_from_trainer
+- grpo
+- trl
+- plasmid
+- biology
+- dna
+license: mit
+---
+
+# PlasmidGPT-RL
+
+This model is a fine-tuned version of [UCL-CSSB/PlasmidGPT-SFT](https://huggingface.co/UCL-CSSB/PlasmidGPT-SFT) using Group Relative Policy Optimization (GRPO).
+
+## Model Description
+
+PlasmidGPT-RL is trained to generate functional plasmid DNA sequences. It was fine-tuned using reinforcement learning with a reward model that evaluates:
+- Presence of valid origins of replication (OriV)
+- Presence of antibiotic resistance genes (ARGs)
+- Absence of problematic repeat sequences
+
+## Training
+
+This model was trained with GRPO using the [TRL library](https://github.com/huggingface/trl).
+
+**Training run**: [Weights & Biases](https://wandb.ai/ucl-cssb/PlasmidRL/runs/4e783zua)
+
+### Training Details
+- **Base model**: UCL-CSSB/PlasmidGPT-SFT
+- **Method**: GRPO (Group Relative Policy Optimization)
+- **Checkpoint**: 800 steps
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("McClain/PlasmidGPT-RL")
+model = AutoModelForCausalLM.from_pretrained("McClain/PlasmidGPT-RL")
+
+# Generate a plasmid sequence
+prompt = "ATG"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(
+    inputs.input_ids,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.95,
+    top_p=0.9
+)
+sequence = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(sequence)
+```
+
+## Framework Versions
+
+- TRL: 0.23.1
+- Transformers: 4.57.0
+- PyTorch: 2.8.0
+
+## Citation
+
+If you use this model, please cite the GRPO paper:
+
+```bibtex
+@article{shao2024deepseekmath,
+    title={{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
+    author={Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
+    year={2024},
+    eprint={arXiv:2402.03300},
+}
+```