README.md

---
datasets:
- GetSoloTech/Code-Reasoning
base_model:
- GetSoloTech/Gemma3-Code-Reasoning-4B
pipeline_tag: text-generation
tags:
- coding
- reasoning
- problem-solving
- algorithms
- python
- c++
- code-reasoning
- competitive-programming
---

# Gemma3-Code-Reasoning-4B-GGUF

This repository contains GGUF (GGML Universal Format) quantized versions of the [GetSoloTech/Gemma3-Code-Reasoning-4B](https://huggingface.co/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
| Model File | Size | Quantization | Use Case |
|------------|------|--------------|----------|
| `Gemma3-Code-Reasoning-4B.f16.gguf` | 7.77 GB | FP16 | Highest quality, requires more VRAM |
| `Gemma3-Code-Reasoning-4B.Q8_0.gguf` | 4.13 GB | Q8_0 | High quality, good balance |
| `Gemma3-Code-Reasoning-4B.Q6_K.gguf` | 3.19 GB | Q6_K | Good quality, moderate VRAM usage |
| `Gemma3-Code-Reasoning-4B.Q5_K_M.gguf` | 2.83 GB | Q5_K_M | Balanced quality and size |
| `Gemma3-Code-Reasoning-4B.Q4_K_M.gguf` | 2.49 GB | Q4_K_M | Good compression, reasonable quality |
| `Gemma3-Code-Reasoning-4B.Q3_K_M.gguf` | 2.1 GB | Q3_K_M | Smaller size, moderate quality |
| `Gemma3-Code-Reasoning-4B.Q2_K.gguf` | 1.73 GB | Q2_K | Smallest size, basic quality |
| `Gemma3-Code-Reasoning-4B.IQ4_XS.gguf` | 2.28 GB | IQ4_XS | Intel optimized, good quality |

## 🔧 Usage

### Using with llama.cpp

```bash
# 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)

```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

| Quantization | Minimum VRAM | Recommended VRAM | CPU RAM |
|--------------|--------------|------------------|---------|
| FP16 | 8 GB | 12 GB | 16 GB |
| Q8_0 | 5 GB | 8 GB | 12 GB |
| Q6_K | 4 GB | 6 GB | 10 GB |
| Q5_K_M | 3 GB | 5 GB | 8 GB |
| Q4_K_M | 3 GB | 4 GB | 6 GB |
| Q3_K_M | 2 GB | 3 GB | 4 GB |
| Q2_K | 2 GB | 2 GB | 3 GB |
| IQ4_XS | 3 GB | 4 GB | 6 GB |

## 📈 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](https://huggingface.co/GetSoloTech/Gemma3-Code-Reasoning-4B)
- **Training Dataset**: [GetSoloTech/Code-Reasoning](https://huggingface.co/datasets/GetSoloTech/Code-Reasoning)
- **Original Gemma Model**: [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it)
- **llama.cpp**: [GitHub Repository](https://github.com/ggerganov/llama.cpp)
- **llama-cpp-python**: [PyPI Package](https://pypi.org/project/llama-cpp-python/)

## 🤝 Contributing

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

- The base model: [Gemma3 Huggingface](https://huggingface.co/google/gemma-3-4b-it)
- The training dataset: [Code-Reasoning Repository](https://huggingface.co/datasets/GetSoloTech/Code-Reasoning)
- The training framework: [Unsloth Documentation](https://github.com/unslothai/unsloth)

## 🙏 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.