Qwen2.5 1.5B S1k 1.1

🚀 Qwen2.5-1.5B-s1k-1.1

This model is a fine - tuned version based on Qwen/Qwen2.5 - 1.5B - Instruct, trained using TRL technology, offering enhanced performance and functionality.

🚀 Quick Start

This section provides a simple example of how to use the model for text generation.

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="rvindra/Qwen2.5-1.5B-s1k-1.1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

✨ Features

• Fine - tuned Model: It is a fine - tuned version of Qwen/Qwen2.5-1.5B-Instruct, which may have better performance on specific tasks.
• Trained with TRL: The model has been trained using TRL, which is a powerful framework for transformer reinforcement learning.

📦 Installation

The document does not provide specific installation steps, so this section is skipped.

💻 Usage Examples

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="rvindra/Qwen2.5-1.5B-s1k-1.1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Advanced Usage

The document does not provide advanced usage examples, so this part is skipped.

📚 Documentation

Model Information

Training Procedure

This model was trained with SFT.

Framework versions

• TRL: 0.16.1
• Transformers: 4.50.3
• Pytorch: 2.5.1
• Datasets: 3.0.2
• Tokenizers: 0.21.0

🔧 Technical Details

The document does not provide in - depth technical details, so this section is skipped.

📄 License

The model is under the specified license.

📚 Citations

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}}
}