初始化项目，由ModelHub XC社区提供模型
Model: Satori-reasoning/Satori-SFT-7B Source: Original Platform
This commit is contained in:
ModelHub XC
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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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library_name: transformers
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pipeline_tag: text-generation
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datasets:
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- Satori-reasoning/Satori_FT_data
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base_model:
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- Qwen/Qwen2.5-Math-7B
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---
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**Satori-7B-SFT** is the SFT model checkpoint for training our RL model [Satori-7B-Round2](https://huggingface.co/Satori-reasoning/Satori-7B-Round2). **Satori-7B-SFT** is only trained with a small-scale format tuning (FT) stage that helps the base LLM to internalize the COAT reasoning format.
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# **Usage**
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```python
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import os
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from tqdm import tqdm
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import torch
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from vllm import LLM, SamplingParams
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def generate(question_list,model_path):
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llm = LLM(
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model=model_path,
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trust_remote_code=True,
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tensor_parallel_size=1,
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)
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sampling_params = SamplingParams(
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max_tokens=4096,
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temperature=0.0,
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n=1,
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skip_special_tokens=True # hide special tokens such as "<|continue|>", "<|reflect|>", and "<|explore|>"
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)
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outputs = llm.generate(question_list, sampling_params, use_tqdm=True)
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completions = [[output.text for output in output_item.outputs] for output_item in outputs]
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return completions
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def prepare_prompt(question):
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prompt = f"<|im_start|>user\nSolve the following math problem efficiently and clearly.\nPlease reason step by step, and put your final answer within \\boxed{{}}.\nProblem: {question}<|im_end|>\n<|im_start|>assistant\n"
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return prompt
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def run():
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model_path = "Satori-reasoning/Satori-7B-SFT"
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all_problems = [
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"which number is larger? 9.11 or 9.9?",
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]
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completions = generate(
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[prepare_prompt(problem_data) for problem_data in all_problems],
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model_path
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)
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for completion in completions:
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print(completion[0])
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if __name__ == "__main__":
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run()
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```
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# **Resources**
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We provide our training datasets:
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- [Full format tuning dataset](https://huggingface.co/datasets/Satori-reasoning/Satori_FT_data) with 300K unique questions.
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- [RL dataset](https://huggingface.co/datasets/Satori-reasoning/Satori_RL_data) with 550K unique questions.
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Please refer to our blog and research paper for more technical details of Satori.
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- [Blog](https://satori-reasoning.github.io/blog/satori/)
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- [Paper](https://arxiv.org/pdf/2502.02508)
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For code, see https://github.com/Satori-reasoning/Satori
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# **Citation**
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If you find our model and data helpful, please cite our paper:
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```
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@misc{shen2025satorireinforcementlearningchainofactionthought,
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title={Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search},
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author={Maohao Shen and Guangtao Zeng and Zhenting Qi and Zhang-Wei Hong and Zhenfang Chen and Wei Lu and Gregory Wornell and Subhro Das and David Cox and Chuang Gan},
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year={2025},
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eprint={2502.02508},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.02508},
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}
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```
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8
train_results.json
Normal file
8
train_results.json
Normal file
@@ -0,0 +1,8 @@
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47
trainer_log.jsonl
Normal file
47
trainer_log.jsonl
Normal file
@@ -0,0 +1,47 @@
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364
trainer_state.json
Normal file
364
trainer_state.json
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Reference in New Issue
Block a user