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---
language:
- en
license: mit
base_model:
- mistralai/Mistral-7B-v0.1
datasets:
- argilla/distilabel-capybara-dpo-7k-binarized
pipeline_tag: text-generation
model-index:
- name: Mistral-ORPO-Capybara-7k
results:
- task:
type: text-generation
dataset:
name: AlpacaEval 2 (LC)
type: AlpacaEval
metrics:
- type: AlpacaEval 2.0
value: 15.88%
name: Win Rate
source:
url: https://tatsu-lab.github.io/alpaca_eval/
name: self-reported
- task:
type: text-generation
dataset:
name: MT-Bench
type: MT-Bench
metrics:
- type: MT-Bench
value: 7.444
name: Score
source:
url: https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/
name: self-reported
---
# **Mistral-ORPO-Capybara-7k (7B)**
**Mistral-ORPO** is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using the *[odds ratio preference optimization (ORPO)](https://arxiv.org/abs/2403.07691)*. With ORPO, the model directly learns the preference without the supervised fine-tuning warmup phase.
**Mistral-ORPO-ORPO-Capybara-7k** is fine-tuned for **2.5 hours on four A100s** exclusively on the **7k** instances of the distilled Capybara paired multi-turn conversation dataset, [argilla/distilabel-capybara-dpo-7k-binarized](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized), by [Argilla](https://huggingface.co/argilla).
- **Github Repository**: https://github.com/xfactlab/orpo
## 👍 **Model Performance**
### 1) AlpacaEval & MT-Bench
|Model Name|Size|Align|MT-Bench|AlpacaEval 2.0 (LC)|
|:--------|:--------------:|:-------------------:|:------------:|:------------:|
|**Mistral-<tt>ORPO</tt>-Capybara-7k**|7B|<tt>ORPO</tt>|7.44|15.9|
|**Mistral-<tt>ORPO</tt>-β**|7B|<tt>ORPO</tt>|7.32|14.7|
|Zephyr β |7B|DPO|7.34|13.2|
|TULU-2-DPO |13B|DPO|7.00|11.6|
|Llama-2-Chat |7B|RLHF|6.27|5.4|
|Llama-2-Chat |13B|RLHF|6.65|8.4|
### 2) IFEval
| **Model Type** | **Prompt-Strict** | **Prompt-Loose** | **Inst-Strict** | **Inst-Loose** |
|--------------------|:-----------------:|:----------------:|:---------------:|:--------------:|
| **Mistral-ORPO-Capybara-7k** | 0.5083 | 0.5083 | 0.5827 | 0.6127 |
| **Mistral-ORPO-⍺** | 0.5009 | 0.5083 | 0.5995 | 0.6163 |
| **Mistral-ORPO-β** | 0.5287 | 0.5564 | 0.6355 | 0.6619 |
## 🗺️ **MT-Bench by Category**
## 🖥️ **Inference**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("kaist-ai/mistral-orpo-capybara-7k")
tokenizer = AutoTokenizer.from_pretrained("kaist-ai/mistral-orpo-capybara-7k")
# Apply chat template
query = [{'role': 'user', 'content': 'Hi! How are you doing?'}]
prompt = tokenizer.apply_chat_template(query, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors='pt')
# Generation with specific configurations
output = model.generate(
**inputs,
max_new_tokens=128,
do_sample=True,
temperature=0.7
)
response = tokenizer.batch_decode(output)
#<|user|>
#Hi! How are you doing?</s>
#<|assistant|>
#I'm doing well, thank you! How are you?</s>
```
## 📎 **Citation**
```
@misc{hong2024orpo,
title={ORPO: Monolithic Preference Optimization without Reference Model},
author={Jiwoo Hong and Noah Lee and James Thorne},
year={2024},
eprint={2403.07691},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |