README.md

---
base_model: INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1
library_name: transformers
model_name: MamayLM-ORPO-align-lora64
tags:
- generated_from_trainer
- trl
- orpo
licence: license
---

# Model Card for MamayLM-ORPO-align-lora64

This model is a fine-tuned version of [INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1](https://huggingface.co/INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1).
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="OpenBabylon/MamayLM-ORPO-align-lora64", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```

## Training procedure

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/polyagent/MamayLM-ORPO/runs/6giyobo5) 


This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).

### Framework versions

- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## Citations

Cite ORPO as:

```bibtex
@article{hong2024orpo,
    title        = {{ORPO: Monolithic Preference Optimization without Reference Model}},
    author       = {Jiwoo Hong and Noah Lee and James Thorne},
    year         = 2024,
    eprint       = {arXiv:2403.07691}
}
```

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}}
}
```