README.md

Quantization made by Richard Erkhov.

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Phi-3-mini-4K-instruct-cpo-simpo - AWQ
- Model creator: https://huggingface.co/Syed-Hasan-8503/
- Original model: https://huggingface.co/Syed-Hasan-8503/Phi-3-mini-4K-instruct-cpo-simpo/




Original model description:
---
license: apache-2.0
---

# Phi-3-mini-4K-instruct with CPO-SimPO

This repository contains the Phi-3-mini-128K-instruct model enhanced with the CPO-SimPO technique. CPO-SimPO combines Contrastive Preference Optimization (CPO) and Simple Preference Optimization (SimPO).

## Introduction

Phi-3-mini-4K-instruct is a model optimized for instruction-based tasks. This approach has demonstrated notable improvements in key benchmarks, pushing the boundaries of AI preference learning.

### What is CPO-SimPO?

CPO-SimPO is a novel technique, which combines elements from CPO and SimPO:

- **Contrastive Preference Optimization (CPO):** Adds a behavior cloning regularizer to ensure the model remains close to the preferred data distribution.
- **Simple Preference Optimization (SimPO):** Incorporates length normalization and target reward margins to prevent the generation of long but low-quality sequences.

### Github

**[CPO-SIMPO](https://github.com/fe1ixxu/CPO_SIMPO)**


## Model Performance

COMING SOON!

### Key Improvements:
- **Enhanced Model Performance:** Significant score improvements, particularly in GSM8K (up by 8.49 points!) and TruthfulQA (up by 2.07 points).
- **Quality Control:** Improved generation of high-quality sequences through length normalization and reward margins.
- **Balanced Optimization:** The BC regularizer helps maintain the integrity of learned preferences without deviating from the preferred data distribution.

## Usage

### Installation

To use this model, you need to install the `transformers` library from Hugging Face.

```bash
pip install transformers
```

### Inference

Here's an example of how to perform inference with the model:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

torch.random.manual_seed(0)

model = AutoModelForCausalLM.from_pretrained(
    "Syed-Hasan-8503/Phi-3-mini-4K-instruct-cpo-simpo", 
    device_map="cuda", 
    torch_dtype="auto", 
    trust_remote_code=True, 
)
tokenizer = AutoTokenizer.from_pretrained("Syed-Hasan-8503/Phi-3-mini-4K-instruct-cpo-simpo")

messages = [
    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 500,
    "return_full_text": False,
    "temperature": 0.0,
    "do_sample": False,
}

output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```