Update README.md

README.md

@@ -1,3 +1,82 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- Satori-reasoning/Satori_FT_data
+base_model:
+- Qwen/Qwen2.5-Math-7B
+---
+
+**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.
+
+
+# **Usage**
+```python
+
+import os
+from tqdm import tqdm
+import torch
+from vllm import LLM, SamplingParams
+
+def generate(question_list,model_path):
+    llm = LLM(
+        model=model_path,
+        trust_remote_code=True,
+        tensor_parallel_size=1,
+    )
+    sampling_params = SamplingParams(
+        max_tokens=4096,
+        temperature=0.0,
+        n=1,
+        skip_special_tokens=True # hide special tokens such as "<|continue|>", "<|reflect|>", and "<|explore|>"
+    )
+    outputs = llm.generate(question_list, sampling_params, use_tqdm=True)
+    completions = [[output.text for output in output_item.outputs] for output_item in outputs]
+    return completions
+
+def prepare_prompt(question):
+    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"
+    return prompt
+    
+def run():
+    model_path = "Satori-reasoning/Satori-7B-SFT"
+    all_problems = [
+        "which number is larger? 9.11 or 9.9?",
+    ]
+    completions = generate(
+        [prepare_prompt(problem_data) for problem_data in all_problems],
+        model_path
+    )
+    
+    for completion in completions:
+        print(completion[0])
+if __name__ == "__main__":
+    run()
+
+```
+
+# **Resources**
+We provide our training datasets:
+  - [Full format tuning dataset](https://huggingface.co/datasets/Satori-reasoning/Satori_FT_data) with 300K unique questions.
+  - [RL dataset](https://huggingface.co/datasets/Satori-reasoning/Satori_RL_data) with 550K unique questions.
+    
+Please refer to our blog and research paper for more technical details of Satori.
+ - [Blog](https://satori-reasoning.github.io/blog/satori/)
+ - [Paper](https://arxiv.org/pdf/2502.02508)
+
+For code, see https://github.com/Satori-reasoning/Satori
+
+# **Citation**
+If you find our model and data helpful, please cite our paper:
+```
+@misc{shen2025satorireinforcementlearningchainofactionthought,
+      title={Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search}, 
+      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},
+      year={2025},
+      eprint={2502.02508},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.02508}, 
+}
+```