57 lines
2.2 KiB
Markdown
57 lines
2.2 KiB
Markdown
---
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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datasets: gsm8k-dataset
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library_name: transformers
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model_name: Qwen2.5-1.5B-Instruct_math_grpo_cosine_0.5_0.5_SEC0.3DRO1.0G0.0_minpTrue_1600
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tags:
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- generated_from_trainer
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- trl
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- grpo
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licence: license
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---
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# Model Card for Qwen2.5-1.5B-Instruct_math_grpo_cosine_0.5_0.5_SEC0.3DRO1.0G0.0_minpTrue_1600
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This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the Countdown dataset.
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It has been trained using [E2H](https://github.com/divelab/E2H-Reasoning) on the top of [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="shubhamprshr/Qwen2.5-1.5B-Instruct_math_grpo_cosine_0.5_0.5_SEC0.3DRO1.0G0.0_minpTrue_1600", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/shubhamprshr27-tamu/dapo_e2h/runs/upy1drqf)
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This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
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### Framework versions
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- TRL: 0.19.1
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- Transformers: 4.53.1
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- Pytorch: 2.7.0
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- Datasets: 3.6.0
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- Tokenizers: 0.21.4
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## Citations
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Cite E2H as:
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```bibtex
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@inproceedings{parashar2026curriculum,
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title = {Curriculum Reinforcement Learning from Easy to Hard Tasks Improves {LLM} Reasoning},
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author = {Parashar, Shubham and Gui, Shurui and Li, Xiner and Ling, Hongyi and Vemuri, Sushil and Olson, Blake and Li, Eric and Zhang, Yu and Caverlee, James and Kalathil, Dileep and Ji, Shuiwang},
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booktitle = {The Fourteenth International Conference on Learning Representations},
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year = {2026},
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url = {https://openreview.net/forum?id=KJvHnl3kUv}
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}
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```
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