Instructions to use GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF",
	filename="Gemma3-Code-Reasoning-4B.IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M

Ollama
How to use GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF with Ollama:
```
ollama run hf.co/GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF to start chatting

Docker Model Runner
How to use GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M
```

Lemonade

How to use GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GetSoloTech/Gemma3-Code-Reasoning-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma3-Code-Reasoning-4B-GGUF-Q4_K_M

List all available models

lemonade list

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

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

# 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

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
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.