Waterhorse
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Llama-3.1-8B-Instruct-NLRL-Breakthrough-Value

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 ---
 library_name: transformers
+license: llama3.1
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+tags:
+- nlrl
 ---
+# Model Card for Llama-3.1-8B-Instruct-NLRL-Breakthrough-Value
 ## Model Details
 ### Model Description
+- **Developed by:** NLRL Team
+- **Model type:** Language Value Function Model for Breakthrough
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** LLaMA-3.1-8B-Instruct
+This model serves as a language value function in Natural Language Reinforcement Learning (NLRL) framework, specifically trained for the Breakthrough game. It evaluates the state through natural language description and provides value assessment.
 ## Uses
 ### Direct Use
+This model can be used as a Breakthrough position evaluator that explains its evaluation through natural language before providing the final assessment. The model generates both reasoning chains and final value judgments.
 ### Out-of-Scope Use
+This model is specifically trained for Breakthrough board state evaluation and should not be used for other games or value assessment tasks.
 ## Training Details
 ### Training Data
+Training data consists of TD data collected through NLRL value learning process, with language-based TD estimates serving as training targets for the value function.
 ### Training Procedure
+- Trained using FSDP (Fully Sharded Data Parallel) across 4 H100 GPUs
+- Learning rate: 2e-5
+- Training epochs per iteration: 2
+- Max sequence length: 1024
 ## Evaluation
+- Demonstrates consistent evaluation capabilities across different game states
+## Model Architecture
+- Base model: LLaMA-3.1-8B-Instruct
+- Input: Text description of Breakthrough board state
+- Output: Chain-of-thought evaluation followed by value assessment
+## Citation
+```bibtex
+@misc{feng2024naturallanguagereinforcementlearning,
+      title={Natural Language Reinforcement Learning},
+      author={Xidong Feng and Ziyu Wan and Haotian Fu and Bo Liu and Mengyue Yang and Girish A. Koushik and Zhiyuan Hu and Ying Wen and Jun Wang},
+      year={2024},
+      eprint={2411.14251},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2411.14251},
+}
+```
 ## Model Card Contact
+[email protected]