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Qwen2-0.5B-XPO - EXL2
- Model creator: https://huggingface.co/trl-lib/
- Original model: https://huggingface.co/trl-lib/Qwen2-0.5B-XPO/


## Available sizes

| Branch | Bits | Description |
| ----- | ---- | ------------ |
| [8_0](https://huggingface.co/trl-lib_-_Qwen2-0.5B-XPO-exl2/tree/8_0) | 8.0 | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/trl-lib_-_Qwen2-0.5B-XPO-exl2/tree/6_5) | 6.5 | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/trl-lib_-_Qwen2-0.5B-XPO-exl2/tree/5_0) | 5.0 | Slightly lower quality vs 6.5, but usable |
| [4_25](https://huggingface.co/trl-lib_-_Qwen2-0.5B-XPO-exl2/tree/4_25) | 4.25 | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/trl-lib_-_Qwen2-0.5B-XPO-exl2/tree/3_5) | 3.5 | Lower quality, only use if you have to. |


## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/trl-lib_-_Qwen2-0.5B-XPO-exl2 Qwen2-0.5B-XPO-6_5
```
With huggingface hub:
```shell
pip3 install huggingface-hub
```
To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch:
Linux:
```shell
huggingface-cli download trl-lib_-_Qwen2-0.5B-XPO-exl2 --revision 6_5 --local-dir Qwen2-0.5B-XPO-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):

```shell
huggingface-cli download trl-lib_-_Qwen2-0.5B-XPO-exl2 --revision 6_5 --local-dir Qwen2-0.5B-XPO-6.5 --local-dir-use-symlinks False
```
    



  Original model description:
  ---
base_model: Qwen/Qwen2-0.5B-Instruct
library_name: transformers
model_name: Qwen2-0.5B-XPO
tags:
- generated_from_trainer
- trl
- xpo
licence: license
---

# Model Card for Qwen2-0.5B-XPO

This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-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="qgallouedec/Qwen2-0.5B-XPO", 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/huggingface/trl/runs/458cjtdo)

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: 4.46.0.dev0
- Pytorch: 2.4.1
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## Citations

Cite XPO as:

```bibtex
@article{jung2024binary,
    title        = {{Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF}},
    author       = {Tengyang Xie and Dylan J. Foster and Akshay Krishnamurthy and Corby Rosset and Ahmed Awadallah and Alexander Rakhlin},
    year         = 2024,
    eprint       = {arXiv:2405.21046}
}
```

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}}
}
```