Create README.md

README.md

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+---
+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.