b681f25400
Model: ali-elganzory/open-sci-ref-v0.02-1.7b-nemotron-hq-300B-16k-SFT-Tulu3-decontaminated Source: Original Platform
57 lines
1.6 KiB
Markdown
57 lines
1.6 KiB
Markdown
---
|
|
base_model: ali-elganzory/open-sci-ref-v0.02-1.7b-nemotron-hq-300B-16384-rope_theta-1M-long_sft_16k
|
|
library_name: transformers
|
|
tags:
|
|
- generated_from_trainer
|
|
- sft
|
|
- trl
|
|
licence: license
|
|
---
|
|
|
|
# Model Card for None
|
|
|
|
This model is a fine-tuned version of [ali-elganzory/open-sci-ref-v0.02-1.7b-nemotron-hq-300B-16384-rope_theta-1M-long_sft_16k](https://huggingface.co/ali-elganzory/open-sci-ref-v0.02-1.7b-nemotron-hq-300B-16384-rope_theta-1M-long_sft_16k).
|
|
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="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
|
|
|
|
- TRL: 0.27.1
|
|
- Transformers: 4.57.6
|
|
- Pytorch: 2.6.0+cu126
|
|
- Datasets: 4.8.4
|
|
- Tokenizers: 0.22.2
|
|
|
|
## 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 Gallou{\'e}dec},
|
|
year = 2020,
|
|
journal = {GitHub repository},
|
|
publisher = {GitHub},
|
|
howpublished = {\url{https://github.com/huggingface/trl}}
|
|
}
|
|
``` |