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Model: Elliott/LUFFY-Qwen-Math-1.5B-Zero Source: Original Platform
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README.md
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---
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base_model:
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- Qwen/Qwen2.5-Math-1.5B
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library_name: transformers
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license: mit
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pipeline_tag: text-generation
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tags:
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- reasoning
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- Zero-RL
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---
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# 📖Introduction
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LUFFY is a reinforcement learning framework that bridges the gap between zero-RL and imitation learning by incorporating off-policy reasoning traces into the training process. Built upon GRPO, LUFFY combines on-policy rollouts with off-policy demonstrations during advantage estimation and introduces **policy shaping** via regularized importance sampling to emphasize low-probability yet crucial actions.
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### Key Highlights:
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- **Off-Policy Guidance:** Seamlessly integrates external reasoning traces to bootstrap learning from stronger models.
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- **Dynamic Balance:** Learns when to imitate and when to explore, adapting over the course of training.
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- **Policy Shaping:** Emphasizes important actions often ignored in standard policy gradients, enabling better generalization.
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---
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## Inference
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Here’s an example of using LUFFY for inference:
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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model_path="Elliott/LUFFY-Qwen-Math-7B-Zero"
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question = "which number is larger? 9.11 or 9.9?"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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messages = [{"role": "user", "content": question}]
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chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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llm = LLM(model=model_path)
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params = SamplingParams(temperature=0.6, max_tokens=8192)
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outputs = llm.generate([chat], params)
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print(outputs[0].outputs[0].text)
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```
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---
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# 📃Evaluation
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| **Model** | **AIME 24** | **AIME 25** | **AMC** | **MATH-500** | **Minerva** | **Olympiad** | **Avg.** |
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|-------|---------|---------|-----|----------|---------|----------|------|
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| Qwen2.5-Math-1.5B-Base | 7.9 | 4.7 | 26.4 | 31.0 | 12.1 | 21.5 | 17.3 |
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| Qwen2.5-Math-1.5B-Instruct | 11.4 | 8.5 | 47.4 | 75.2 | 27.6 | 38.7 | 34.8 |
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| SFT | 15.2 | **14.3** | 43.5 | 74.8 | **30.9** | 36.9 | 40.3 |
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| On-Policy RL | 12.6 | 6.5 | 42.6 | 68.8 | 22.1 | 34.4 | 36.1 |
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| **LUFFY-1.5B-Zero** | **15.2** | 12.7 | **46.8** | **79.4** | 26.5 | **42.4** | **42.1** |
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---
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# 🌻Acknowledgement
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LUFFY builds upon [veRL](https://github.com/volcengine/verl) and [deepscaler](https://github.com/agentica-project/rllm), and utilizes [vLLM](https://github.com/vllm-project/vllm) for inference. We utilize [Math-Verify](https://github.com/huggingface/Math-Verify) for math reasoning evaluation. We thank the open-source community for datasets and backbones, including [NuminaMath](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT), [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k), [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math), and [DeepSeek-R1](https://github.com/deepseek-ai/deepseek-r1) model.
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Code: https://github.com/ElliottYan/LUFFY
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# Citation
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If you find our model, data, or evaluation code useful, please kindly cite our paper:
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```bib
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@misc{luffy,
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title={Learning to Reason under Off-Policy Guidance},
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author={Jianhao Yan and Yafu Li and Zican Hu and Zhi Wang and Ganqu Cui and Xiaoye Qu and Yu Cheng and Yue Zhang},
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year={2025},
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eprint={2504.14945},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2504.14945},
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}
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```
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config.json
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|
||||
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|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
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|
||||
"<|box_start|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"<|video_pad|>"
|
||||
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|
||||
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|
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|
||||
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|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
||||
207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
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|
||||
"151643": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"bos_token": null,
|
||||
"chat_template": "{{ bos_token }}Your task is to follow a systematic, thorough reasoning process before providing the final solution. This involves analyzing, summarizing, exploring, reassessing, and refining your thought process through multiple iterations. Structure your response into two sections: Thought and Solution. In the Thought section, present your reasoning using the format: “<think>\n {thoughts} </think>\n”. Each thought should include detailed analysis, brainstorming, verification, and refinement of ideas. After “</think>\n,” in the Solution section, provide the final, logical, and accurate answer, clearly derived from the exploration in the Thought section. If applicable, include the answer in \boxed{} for closed-form results like multiple choices or mathematical solutions. {% for message in messages %}{% if message['role'] == 'user' %}User: This is the problem:\n{{ message['content'] }}\n{% elif message['role'] == 'assistant' %}Assistant: <think>\n{{ message['content'] }}\n{% endif %}{% endfor %}{% if add_generation_prompt %}Assistant: <think>\n{% endif %}",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
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|
||||
}
|
||||
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