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
🔍 Model on Hugging Face
🧠 Model Details
- Model Type: Hybrid Language Model (Merged for Code Generation)
- Parent Models:
- 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! 🚀