import random
from transformers import pipeline
from datasets import load_dataset

ds = load_dataset("trl-lib/Capybara")

pipe = pipeline("text-generation", model="dhananjayanjali/Qwen2.5-0.5B-SFT")

sample = random.choice(ds['train'])


prompt = sample['messages'][0]['content']

#print("\nsample:\n", sample["response"])

print("PROMPT:", prompt)

output = pipe(prompt, max_new_tokens=100)[0]["generated_text"]
print("\nMODEL RESPONSE:\n", output)
print("\nEXPECTED RESPONSE:\n", sample)

Model Card for Qwen2.5-0.5B-SFT

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B. It has been trained using 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="dhananjayanjali/Qwen2.5-0.5B-SFT", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

Visualize in Weights & Biases

This model was trained with SFT.

Framework versions

TRL: 0.23.1
Transformers: 4.56.2
Pytorch: 2.8.0+cu126
Datasets: 4.0.0
Tokenizers: 0.22.1

Citations

Cite TRL as:

bibtex

@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 Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}