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golden-goose-qwen2.5-1.5b-i…/README.md
ModelHub XC dfa9e93101 初始化项目,由ModelHub XC社区提供模型
Model: cjiao/golden-goose-qwen2.5-1.5b-instruct-random
Source: Original Platform
2026-04-27 22:50:18 +08:00

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---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: golden-goose-qwen2.5-1.5b-instruct-random
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for golden-goose-qwen2.5-1.5b-instruct-random
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
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?"
generator = pipeline("text-generation", model="cjiao/golden-goose-qwen2.5-1.5b-instruct-random", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<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/cathyjiao/golden_goose/runs/citkwyq8)
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).
### Framework versions
- TRL: 1.1.0
- Transformers: 4.57.6
- Pytorch: 2.10.0
- Datasets: 4.8.4
- Tokenizers: 0.22.2
## Citations
Cite GRPO as:
```bibtex
@article{shao2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}
```