MatteoKhan/CodeLlama-7B-Merged-Python

🚀 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
🔍 Model on Hugging Face

🧠 Model Details

• Model Type: Hybrid Language Model (Merged for Code Generation)
• Parent Models:
  • CodeLlama-7B
  • CodeLlama-7B-Python
• 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:

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

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.

🎉 Happy Coding! 🚀