README.md

---
language:
- en
license: other
library_name: transformers
tags:
- orpo
- llama 3
- rlhf
- sft
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- mlabonne/orpo-dpo-mix-40k
---

# OrpoLlama-3-8B

![](https://i.imgur.com/ZHwzQvI.png)

This is an ORPO fine-tune of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k) created for [this article](https://huggingface.co/blog/mlabonne/orpo-llama-3).

It's a successful fine-tune that follows the ChatML template!

**Try the demo**: https://huggingface.co/spaces/mlabonne/OrpoLlama-3-8B

## 🔎 Application

This model uses a context window of 8k. It was trained with the ChatML template.

## ⚡ Quantized models

Thanks to bartowski, solidrust, and LoneStriker for the quantized models.

* **GGUF**: https://huggingface.co/bartowski/OrpoLlama-3-8B-GGUF
* **AWQ**: https://huggingface.co/solidrust/OrpoLlama-3-8B-AWQ
* **EXL2**:
  * https://huggingface.co/LoneStriker/OrpoLlama-3-8B-3.0bpw-h6-exl2
  * https://huggingface.co/LoneStriker/OrpoLlama-3-8B-4.0bpw-h6-exl2
  * https://huggingface.co/LoneStriker/OrpoLlama-3-8B-5.0bpw-h6-exl2
  * https://huggingface.co/LoneStriker/OrpoLlama-3-8B-6.0bpw-h6-exl2
  * https://huggingface.co/LoneStriker/OrpoLlama-3-8B-8.0bpw-h8-exl2

## 🏆 Evaluation

### Nous

OrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.

Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval), see the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).

| Model                                                                                                                                                                     |   Average |   AGIEval |   GPT4All | TruthfulQA |  Bigbench |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------: | --------: | --------: | ---------: | --------: |
| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) |     51.34 |     41.22 |     69.86 |      51.65 |     42.64 |
| [**mlabonne/OrpoLlama-3-8B**](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f)                     | **48.63** | **34.17** | **70.59** | **52.39** | **37.36** |
| [mlabonne/OrpoLlama-3-8B-1k](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/f41dad371d1781d0434a4672fd6f0b82)                      | 46.76     | 31.56     | 70.19     |  48.11     | 37.17     |
| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847)                   |     45.42 |      31.1 |     69.95 |      43.91 |      36.7 |

`mlabonne/OrpoLlama-3-8B-1k` corresponds to a version of this model trained on 1K samples (you can see the parameters in [this article](https://huggingface.co/blog/mlabonne/orpo-llama-3)). The current version was trained on a full epoch.

### Open LLM Leaderboard

![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/5auE1oyKJ-TsvrUVeQBYr.png)

## 📈 Training curves

You can find the experiment on W&B at [this address](https://wandb.ai/mlabonne/DPO/runs/vxnmq24z/workspace?nw=nwusermlabonne).

![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/zm71HyZiG96YY1GUtpfHq.png)

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/OrpoLlama-3-8B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```