base_model	Qwen/Qwen2.5-Coder-1.5B-Instruct
library_name	peft
model_name	fast
tags	base_model:adapter:Qwen/Qwen2.5-Coder-1.5B-Instruct lora sft transformers trl
licence	license
pipeline_tag	text-generation

Model Card for fast

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-1.5B-Instruct. It has been trained using TRL.

Quick start

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="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

• PEFT 0.18.1
• TRL: 0.29.1
• Transformers: 5.0.0
• Pytorch: 2.10.0+cu128
• Datasets: 4.8.3
• Tokenizers: 0.22.2

Citations

Cite TRL as:

@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}
}
```# Deep-Learning-Competition-1