README.md

Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

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xpo-qwen2 - GGUF
- Model creator: https://huggingface.co/qgallouedec/
- Original model: https://huggingface.co/qgallouedec/xpo-qwen2/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [xpo-qwen2.Q2_K.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q2_K.gguf) | Q2_K | 0.32GB |
| [xpo-qwen2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.IQ3_XS.gguf) | IQ3_XS | 0.32GB |
| [xpo-qwen2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.IQ3_S.gguf) | IQ3_S | 0.32GB |
| [xpo-qwen2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q3_K_S.gguf) | Q3_K_S | 0.32GB |
| [xpo-qwen2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.IQ3_M.gguf) | IQ3_M | 0.32GB |
| [xpo-qwen2.Q3_K.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q3_K.gguf) | Q3_K | 0.33GB |
| [xpo-qwen2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q3_K_M.gguf) | Q3_K_M | 0.33GB |
| [xpo-qwen2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q3_K_L.gguf) | Q3_K_L | 0.34GB |
| [xpo-qwen2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.IQ4_XS.gguf) | IQ4_XS | 0.33GB |
| [xpo-qwen2.Q4_0.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q4_0.gguf) | Q4_0 | 0.33GB |
| [xpo-qwen2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.IQ4_NL.gguf) | IQ4_NL | 0.33GB |
| [xpo-qwen2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q4_K_S.gguf) | Q4_K_S | 0.36GB |
| [xpo-qwen2.Q4_K.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q4_K.gguf) | Q4_K | 0.37GB |
| [xpo-qwen2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q4_K_M.gguf) | Q4_K_M | 0.37GB |
| [xpo-qwen2.Q4_1.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q4_1.gguf) | Q4_1 | 0.35GB |
| [xpo-qwen2.Q5_0.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q5_0.gguf) | Q5_0 | 0.37GB |
| [xpo-qwen2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q5_K_S.gguf) | Q5_K_S | 0.38GB |
| [xpo-qwen2.Q5_K.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q5_K.gguf) | Q5_K | 0.39GB |
| [xpo-qwen2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q5_K_M.gguf) | Q5_K_M | 0.39GB |
| [xpo-qwen2.Q5_1.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q5_1.gguf) | Q5_1 | 0.39GB |
| [xpo-qwen2.Q6_K.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q6_K.gguf) | Q6_K | 0.47GB |
| [xpo-qwen2.Q8_0.gguf](https://huggingface.co/RichardErkhov/qgallouedec_-_xpo-qwen2-gguf/blob/main/xpo-qwen2.Q8_0.gguf) | Q8_0 | 0.49GB |




Original model description:
---
base_model: Qwen/Qwen2-0.5B-Instruct
datasets: trl-lib/ultrafeedback-prompt
library_name: transformers
model_name: xpo-qwen2
tags:
- trl
- generated_from_trainer
- xpo
licence: license
---

# Model Card for xpo-qwen2

This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [trl-lib/ultrafeedback-prompt](https://huggingface.co/datasets/trl-lib/ultrafeedback-prompt) dataset.
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="qgallouedec/xpo-qwen2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=500)[0]
print(output["generated_text"][1]["content"])
```

## 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/huggingface/huggingface/runs/bg6y6mom)

This model was trained with XPO, a method introduced in [Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF](https://huggingface.co/papers/2405.21046).

### Framework versions

- TRL: 0.12.0.dev0
- Transformers: 4.45.0.dev0
- Pytorch: 2.4.1
- Datasets: 3.0.0
- Tokenizers: 0.19.1

## Citations

Cite XPO as:
    
```bibtex
@article{jung2024binary,
    title        = {{Binary Classifier Optimization for Large Language Model Alignment}},
    author       = {Seungjae Jung and Gunsoo Han and Daniel Wontae Nam and Kyoung{-}Woon On},
    year         = 2024,
    eprint       = {arXiv:2404.04656}
}
```

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}}
}
```