初始化项目，由ModelHub XC社区提供模型
Model: UofTCSSLab/C1-4B Source: Original Platform
This commit is contained in:
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-4B-Instruct-2507
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- chess
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- reasoning
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- chess-puzzles
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- qwen3
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- reinforcement-learning
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- dapo
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---
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# C1: Grounded Chess Reasoning in Language Models via Master Distillation
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[](https://github.com/CSSLab/C1) [](https://arxiv.org/abs/2603.20510) [](https://huggingface.co/datasets/UofTCSSLab/C1-data)
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The **final (SFT + RL)** model of **C1**. Given a chess position,
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the model reasons step by step in natural language and ends with a single best
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move in UCI notation.
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This is the **final (SFT + RL) model**. The SFT-stage checkpoint is
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[`UofTCSSLab/C1-SFT-4B`](https://huggingface.co/UofTCSSLab/C1-SFT-4B).
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## Results
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900-puzzle test set, greedy pass@1, `FINAL_ANSWER` exact-match UCI:
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| stage | accuracy |
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|---|---|
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| SFT (base for RL) | 42.3% |
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| **RL (this model)** | **48.3%** |
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Average response length ~169 tokens.
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## Usage
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The prompt gives the FEN, piece positions, and legal moves, then asks for a
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step-by-step analysis ending in `FINAL_ANSWER: <uci_move>`. **Greedy decoding
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(temperature 0) is recommended**.
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```python
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# pip install chess
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import chess
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def build_prompt(fen: str) -> str:
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board = chess.Board(fen)
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names = {1: "Pawn", 2: "Knight", 3: "Bishop", 4: "Rook", 5: "Queen", 6: "King"}
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pieces = {}
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for sq in chess.SQUARES:
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p = board.piece_at(sq)
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if p:
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key = f"{'White' if p.color else 'Black'} {names[p.piece_type]}"
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pieces.setdefault(key, []).append(chess.square_name(sq))
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order = [f"{c} {t}" for c in ("White", "Black")
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for t in ("King", "Queen", "Rook", "Bishop", "Knight", "Pawn")]
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arrangement = ", ".join(f"{k}: {sorted(pieces[k])}" for k in order if k in pieces)
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legal = ", ".join(m.uci() for m in board.legal_moves)
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return (
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f"You are given a chess position in FEN: {fen}.\n"
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f"Piece positions: {arrangement}\n"
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f"Legal moves: {legal}\n"
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"Find the best move for the side to play.\n"
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"Analyze step by step and explain your reasoning.\n"
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"Finish with a single line formatted EXACTLY as:\n"
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"FINAL_ANSWER: <answer>\n"
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"Use UCI notation (e.g., e2e4, c2b1q) for the final answer."
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)
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MODEL_ID = "UofTCSSLab/C1-4B"
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FEN = "2kr3r/ppp2Npp/2nbp3/6N1/2PP2n1/4B2q/PP2BP2/R2Q1RK1 b - - 2 15"
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messages = [{"role": "user", "content": build_prompt(FEN)}]
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```
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### Transformers
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```python
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# pip install transformers torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="bfloat16", device_map="auto")
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ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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out = model.generate(ids, max_new_tokens=1024, do_sample=False) # greedy
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print(tok.decode(out[0, ids.shape[-1]:], skip_special_tokens=True))
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# ... step-by-step reasoning ...
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# FINAL_ANSWER: h3h2
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```
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### vLLM
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```python
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# pip install vllm
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from vllm import LLM, SamplingParams
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llm = LLM(model=MODEL_ID, dtype="bfloat16")
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sampling = SamplingParams(temperature=0.0, max_tokens=1024) # greedy
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out = llm.chat(messages, sampling_params=sampling)
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print(out[0].outputs[0].text)
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# ... step-by-step reasoning ...
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# FINAL_ANSWER: h3h2
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```
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## Citation
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```bibtex
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@article{tang2026grounded,
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title={Grounded Chess Reasoning in Language Models via Master Distillation},
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author={Tang, Zhenwei and Wen, Qianfeng and Grief-Albert, Seth and Elgabra, Yahya and Yang, Blair and Dong, Honghua and Anderson, Ashton},
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journal={arXiv preprint arXiv:2603.20510},
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year={2026}
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}
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```
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config.json
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config.json
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}
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BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
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Normal file
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239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
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"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 262144,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"add_bos_token": false
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user