File size: 1,536 Bytes
219f07e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: llama-3.1-8B-instruct-combined
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama-3.1-8B-instruct-combined
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co./meta-llama/Llama-3.1-8B-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="ketchup123/llama-3.1-8B-instruct-combined", 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: 0.12.0
- Transformers: 4.46.1
- Pytorch: 2.4.1+cu121
- Datasets: 3.1.0
- Tokenizers: 0.20.2
## 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}}
}
``` |