metadata

license: apache-2.0
datasets:
  - GetSoloTech/Code-Reasoning
language:
  - en
base_model:
  - GetSoloTech/Qwen3-Code-Reasoning-4B
pipeline_tag: text-generation
tags:
  - coding
  - reasoning
  - problem-solving
  - algorithms
  - python
  - c++

GetSoloTech/Qwen3-Code-Reasoning-4B-GGUF

This is the GGUF quantized version of the Qwen3-Code-Reasoning-4B model, specifically optimized for competitive programming and code reasoning tasks. This 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 Inference: GGUF format enables fast inference on CPU and GPU
Multiple Quantization Levels: Available in various precision levels for different hardware requirements

Dataset Statistics

Split: Python
Source: High-quality competitive programming problems from TACO, APPS, CodeContests, and Codeforces
Quality Filter: Only correctly solved problems with ≥85% test case pass rates

🔧 Usage

Using with llama.cpp

# Download the model (choose your preferred quantization)
wget https://huggingface.co/GetSoloTech/Qwen3-Code-Reasoning-4B-GGUF/resolve/main/qwen3-code-reasoning-4b.Q4_K_M.gguf

# Run inference
./llama.cpp -m qwen3-code-reasoning-4b.Q4_K_M.gguf -n 4096 --repeat_penalty 1.1 -p "You are an expert competitive programmer. Read the problem and produce a correct, efficient solution. Include reasoning if helpful.\n\nProblem: Your programming problem here..."

Using with Python (llama-cpp-python)

from llama_cpp import Llama

# Load the model
llm = Llama(
    model_path="./qwen3-code-reasoning-4b.Q4_K_M.gguf",
    n_ctx=4096,
    n_threads=4
)

# Prepare input for competitive programming problem
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..."""

# Generate solution
output = llm(
    prompt,
    max_tokens=4096,
    temperature=0.7,
    top_p=0.8,
    top_k=20,
    repeat_penalty=1.1
)

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

Using with Ollama

# Create a Modelfile
cat > Modelfile << EOF
FROM ./qwen3-code-reasoning-4b.Q4_K_M.gguf
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
"""
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 20
PARAMETER repeat_penalty 1.1
EOF

# Create and run the model
ollama create qwen3-code-reasoning -f Modelfile
ollama run qwen3-code-reasoning "Solve this competitive programming problem: [your problem here]"

📊 Available Quantizations

Quantization	Size	Memory Usage	Quality	Use Case
Q3_K_M	2.08 GB	~3 GB	Good	CPU inference, limited memory
Q4_K_M	2.5 GB	~4 GB	Better	Balanced performance/memory
Q5_K_M	2.89 GB	~5 GB	Very Good	High quality, moderate memory
Q6_K	3.31 GB	~6 GB	Excellent	High quality, more memory
Q8_0	4.28 GB	~8 GB	Best	Maximum quality, high memory
F16	8.05 GB	~16 GB	Original	Maximum quality, GPU recommended

📈 Performance Expectations

This GGUF quantized model maintains the performance characteristics of the original finetuned model:

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

🎛️ Recommended Settings

For Code Generation

Temperature: 0.7
Top-p: 0.8
Top-k: 20
Max New Tokens: 4096 (adjust based on problem complexity)
Repeat Penalty: 1.1

For Reasoning Tasks

Temperature: 0.6
Top-p: 0.95
Top-k: 20
Max New Tokens: 8192 (for complex reasoning)
Repeat Penalty: 1.1

🛠️ Hardware Requirements

Minimum Requirements

RAM: 4 GB (for Q3_K_M quantization)
Storage: 2.5 GB free space
CPU: Multi-core processor recommended

Recommended Requirements

RAM: 8 GB or more
Storage: 5 GB free space
GPU: NVIDIA GPU with 4GB+ VRAM (optional, for faster inference)

🤝 Contributing

This GGUF model was converted from the original LoRA-finetuned model. For questions about:

The original model: GetSoloTech/Qwen3-Code-Reasoning-4B
The base model: Qwen3 GitHub
The training dataset: Code-Reasoning Repository
The training framework: Unsloth Documentation

📄 License

This model follows the same license as the base model (Apache 2.0). Please refer to the base model license for details.

🙏 Acknowledgments

Qwen 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 model, please open an issue in the repository.

Note: This model is specifically optimized for competitive programming and code reasoning tasks. The GGUF format enables efficient inference on various hardware configurations while maintaining the model's reasoning capabilities.