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license: mit
language:
- en
base_model:
- codellama/CodeLlama-7b-hf
- codellama/CodeLlama-7b-Python-hf
library_name: transformers
tags:
- mergekit
- merged-model
- codellama
- programming
- language-model
---
# 🚀 CodeLlama-Hybrid-7B: Optimized for Code Generation
## 📌 Overview
**CodeLlama-Hybrid-7B** is an **experimental hybrid language model** that merges the capabilities of two CodeLlama variants. Built using **MergeKit**, this model is optimized for programming-related tasks, balancing efficiency and performance in code generation and understanding.
🔗 **Created by**: Matteo Khan
🎓 **Affiliation**: Apprentice at TW3 Partners (Generative AI Research)
📍 **License**: MIT
🔗 [Connect with me on LinkedIn](https://www.linkedin.com/in/matteo-khan-a10309263/)
🔍 [Model on Hugging Face](https://huggingface.co/MatteoKhan/CodeLlama-Hybrid-7B)
## 🧠 Model Details
- **Model Type**: Hybrid Language Model (Merged for Code Generation)
- **Parent Models**:
- [CodeLlama-7B](https://huggingface.co/codellama/CodeLlama-7b-hf)
- [CodeLlama-7B-Python](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)
- **Merging Technique**: Linear Merge (MergeKit)
- **Tokenizer Source**: `codellama/CodeLlama-7b-hf`
## 🎯 Intended Use
This model is designed for **code-related tasks** and experimentation in hybrid model optimization. Possible applications include:
- ✅ Code Generation
- ✅ Code Completion & Assistance
- ✅ Code Understanding & Refactoring
- ✅ Exploration of Model Merging Effects on Programming Tasks
## ⚠️ Limitations & Considerations
While **CodeLlama-Hybrid-7B** provides enhanced code generation capabilities, it inherits some limitations from its parent models:
- ❌ May produce **incorrect or insecure** code
- ⚠️ Can generate **biased, offensive, or inappropriate** content
- 🔄 Merging may introduce **unpredictable behaviors**
- 📉 Performance may **vary depending on the programming language and context**
## 🔬 Merging Process & Configuration
This is **not a newly trained model**, but rather a merge of existing models using the following configuration:
```yaml
merge_method: linear
dtype: float16
allow_crimes: true
models:
- model: "codellama/CodeLlama-7b-hf"
parameters:
t: 1.0
weight: 0.5
- model: "codellama/CodeLlama-7b-Python-hf"
parameters:
t: 1.0
weight: 0.5
parameters:
normalize: true
int8_mask: false
ignore_mismatched_sizes: true
layers:
- pattern: "model.*"
tokenizer_source: "codellama/CodeLlama-7b-hf"
```
📊 **No formal evaluation** has been conducted yet. Users are encouraged to **benchmark and share feedback**!
## 🌍 Environmental Impact
By utilizing **model merging** instead of training from scratch, **CodeLlama-Hybrid-7B** significantly reduces computational and environmental costs.
## 🚀 How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "MatteoKhan/CodeLlama-Hybrid-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Write a Python function to calculate Fibonacci numbers."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
📩 **Feedback & Contact**: Reach out via [Hugging Face](https://huggingface.co/MatteoKhan).
🎉 **Happy Coding!** 🚀
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