初始化项目，由ModelHub XC社区提供模型

Model: UofTCSSLab/C1-4B Source: Original Platform
2026-09-01 06:46:18 +08:00
commit d29a8b43e5
11 changed files with 152276 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,117 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- chess
+- reasoning
+- chess-puzzles
+- qwen3
+- reinforcement-learning
+- dapo
+---
+
+# C1: Grounded Chess Reasoning in Language Models via Master Distillation
+
+[![Code](https://img.shields.io/badge/Code-GitHub-181717?logo=github)](https://github.com/CSSLab/C1) [![arXiv](https://img.shields.io/badge/arXiv-2603.20510-b31b1b?logo=arxiv)](https://arxiv.org/abs/2603.20510) [![Hugging Face](https://img.shields.io/badge/HuggingFace-Dataset-yellow?link=https://huggingface.co/datasets/UofTCSSLab/C1-data)](https://huggingface.co/datasets/UofTCSSLab/C1-data)
+
+The **final (SFT + RL)** model of **C1**. Given a chess position,
+the model reasons step by step in natural language and ends with a single best
+move in UCI notation.
+
+This is the **final (SFT + RL) model**. The SFT-stage checkpoint is
+[`UofTCSSLab/C1-SFT-4B`](https://huggingface.co/UofTCSSLab/C1-SFT-4B).
+
+## Results
+
+900-puzzle test set, greedy pass@1, `FINAL_ANSWER` exact-match UCI:
+
+| stage | accuracy |
+|---|---|
+| SFT (base for RL) | 42.3% |
+| **RL (this model)** | **48.3%** |
+
+Average response length ~169 tokens.
+
+## Usage
+
+The prompt gives the FEN, piece positions, and legal moves, then asks for a
+step-by-step analysis ending in `FINAL_ANSWER: <uci_move>`. **Greedy decoding
+(temperature 0) is recommended**.
+
+```python
+# pip install chess
+import chess
+
+def build_prompt(fen: str) -> str:
+    board = chess.Board(fen)
+    names = {1: "Pawn", 2: "Knight", 3: "Bishop", 4: "Rook", 5: "Queen", 6: "King"}
+    pieces = {}
+    for sq in chess.SQUARES:
+        p = board.piece_at(sq)
+        if p:
+            key = f"{'White' if p.color else 'Black'} {names[p.piece_type]}"
+            pieces.setdefault(key, []).append(chess.square_name(sq))
+    order = [f"{c} {t}" for c in ("White", "Black")
+             for t in ("King", "Queen", "Rook", "Bishop", "Knight", "Pawn")]
+    arrangement = ", ".join(f"{k}: {sorted(pieces[k])}" for k in order if k in pieces)
+    legal = ", ".join(m.uci() for m in board.legal_moves)
+    return (
+        f"You are given a chess position in FEN: {fen}.\n"
+        f"Piece positions: {arrangement}\n"
+        f"Legal moves: {legal}\n"
+        "Find the best move for the side to play.\n"
+        "Analyze step by step and explain your reasoning.\n"
+        "Finish with a single line formatted EXACTLY as:\n"
+        "FINAL_ANSWER: <answer>\n"
+        "Use UCI notation (e.g., e2e4, c2b1q) for the final answer."
+    )
+
+MODEL_ID = "UofTCSSLab/C1-4B"
+FEN = "2kr3r/ppp2Npp/2nbp3/6N1/2PP2n1/4B2q/PP2BP2/R2Q1RK1 b - - 2 15"
+messages = [{"role": "user", "content": build_prompt(FEN)}]
+```
+
+### Transformers
+
+```python
+# pip install transformers torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="bfloat16", device_map="auto")
+
+ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(ids, max_new_tokens=1024, do_sample=False)  # greedy
+print(tok.decode(out[0, ids.shape[-1]:], skip_special_tokens=True))
+# ... step-by-step reasoning ...
+# FINAL_ANSWER: h3h2
+```
+
+### vLLM
+
+```python
+# pip install vllm
+from vllm import LLM, SamplingParams
+
+llm = LLM(model=MODEL_ID, dtype="bfloat16")
+sampling = SamplingParams(temperature=0.0, max_tokens=1024)  # greedy
+out = llm.chat(messages, sampling_params=sampling)
+print(out[0].outputs[0].text)
+# ... step-by-step reasoning ...
+# FINAL_ANSWER: h3h2
+```
+
+## Citation
+
+```bibtex
+@article{tang2026grounded,
+  title={Grounded Chess Reasoning in Language Models via Master Distillation},
+  author={Tang, Zhenwei and Wen, Qianfeng and Grief-Albert, Seth and Elgabra, Yahya and Yang, Blair and Dong, Honghua and Anderson, Ashton},
+  journal={arXiv preprint arXiv:2603.20510},
+  year={2026}
+}
+```