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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+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'])
+```
+