Qwen2.5 0.5B Instruct Gensyn Swarm Feathered Giant Ostrich

🚀 Qwen2.5-0.5B-Instruct-Gensyn-Swarm-feathered_giant_ostrich

This model is a fine - tuned version of Gensyn/Qwen2.5-0.5B-Instruct, trained using TRL. It offers enhanced performance in text - generation tasks.

🚀 Quick Start

Basic Usage

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="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-feathered_giant_ostrich", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

✨ Features

• Fine - Tuned: Based on Gensyn/Qwen2.5-0.5B-Instruct, it has been fine - tuned to improve performance.
• Trained with TRL: Utilizes TRL for training, enabling reinforcement learning techniques.

📦 Installation

This model uses the transformers library. You can install it using the following command:

pip install transformers

📚 Documentation

Training procedure

This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Framework versions

📄 License

This model is under the license license.

📚 Citations

Cite GRPO as:

@article{zhihong2024deepseekmath,
    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:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}