README.md

---

base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: logsQwen2.5-0.5B-Instruct-math-gsm8k
tags:
- generated_from_trainer
- trl
- sft
licence: license
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---


# Model Card for logsQwen2.5-0.5B-Instruct-math-gsm8k

This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-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="neurocoder/logsQwen2.5-0.5B-Instruct-math-gsm8k", 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

- TRL: 0.14.0
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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 Gallouédec},

	year         = 2020,

	journal      = {GitHub repository},

	publisher    = {GitHub},

	howpublished = {\url{https://github.com/huggingface/trl}}

}

```