GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF

Gemma3-Code-Reasoning-4B-GGUF

This repository contains GGUF (GGML Universal Format) quantized versions of the GetSoloTech/Gemma3-Code-Reasoning-4B model, optimized for local inference with various quantization levels to balance performance and resource usage.

🎯 Model Overview

This is a LoRA-finetuned version of gemma-3-4b-it specifically optimized for competitive programming and code reasoning tasks. The model has been trained on the high-quality Code-Reasoning dataset to enhance its capabilities in solving complex programming problems with detailed reasoning.

🚀 Key Features

• —Enhanced Code Reasoning: Specifically trained on competitive programming problems
• —Thinking Capabilities: Inherits the advanced reasoning capabilities from the base model
• —High-Quality Solutions: Trained on solutions with ≥85% test case pass rates
• —Structured Output: Optimized for generating well-reasoned programming solutions
• —Efficient Training: Uses LoRA adapters for efficient parameter updates
• —Multiple Quantization Levels: Available in various GGUF formats for different hardware capabilities

📁 Available GGUF Models

🔧 Usage

Using with llama.cpp

# Download a GGUF model file
wget https://huggingface.co/GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF/resolve/main/Gemma3-Code-Reasoning-4B.Q4_K_M.gguf

# Run inference with llama.cpp
./llama.cpp/main -m Gemma3-Code-Reasoning-4B.Q4_K_M.gguf -n 4096 --repeat_penalty 1.1 -p "You are an expert competitive programmer. Solve this problem: [YOUR_PROBLEM_HERE]"

Using with Python (llama-cpp-python)

from llama_cpp import Llama

# Load the model
llm = Llama(
    model_path="./Gemma3-Code-Reasoning-4B.Q4_K_M.gguf",
    n_ctx=4096,
    n_threads=4
)

# Prepare the prompt
prompt = """You are an expert competitive programmer. Read the problem and produce a correct, efficient solution. Include reasoning if helpful.

Problem: [YOUR_PROGRAMMING_PROBLEM_HERE]

Solution:"""

# Generate response
output = llm(
    prompt,
    max_tokens=4096,
    temperature=1.0,
    top_p=0.95,
    top_k=64,
    repeat_penalty=1.1
)

print(output['choices'][0]['text'])

🎛️ Recommended Settings

• —Temperature: 1.0
• —Top-p: 0.95
• —Top-k: 64
• —Max New Tokens: 4096 (adjust based on problem complexity)
• —Repeat Penalty: 1.1

💻 Hardware Requirements

📈 Performance Expectations

This finetuned model is expected to show improved performance on:

• —Competitive Programming Problems: Better understanding of problem constraints and requirements
• —Code Generation: More accurate and efficient solutions
• —Reasoning Quality: Enhanced step-by-step reasoning for complex problems
• —Solution Completeness: More comprehensive solutions with proper edge case handling

🔗 Related Resources

• —Base Model: GetSoloTech/Gemma3-Code-Reasoning-4B
• —Training Dataset: GetSoloTech/Code-Reasoning
• —Original Gemma Model: google/gemma-3-4b-it
• —llama.cpp: GitHub Repository
• —llama-cpp-python: PyPI Package

🤝 Contributing

This model was created using the Unsloth framework and the Code-Reasoning dataset. For questions about:

• —The base model: Gemma3 Huggingface
• —The training dataset: Code-Reasoning Repository
• —The training framework: Unsloth Documentation

🙏 Acknowledgments

• —Gemma Team for the excellent base model
• —Unsloth Team for the efficient training framework
• —NVIDIA Research for the original OpenCodeReasoning-2 dataset
• —llama.cpp community for the GGUF format and tools

📞 Contact

For questions about this GGUF converted model, please open an issue in the repository.

Note: This model is specifically optimized for competitive programming and code reasoning tasks. Choose the appropriate quantization level based on your hardware capabilities and quality requirements.