A finetuned version of google/gemma-3-4b-it 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.

🎯 Model Overview

This model is a LoRA-finetuned version of gemma-3-4b-it with the following specifications:

Base Model: gemma-3-4b-it (4.0B parameters)
Training Method: LoRA (Low-Rank Adaptation)
Training Dataset: GetSoloTech/Code-Reasoning
Training Framework: Unsloth with QLoRA
Context Length: 4096 tokens
Model Type: Causal Language Model with Thinking Capabilities

🚀 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 ≥50% test case pass rates
Structured Output: Optimized for generating well-reasoned programming solutions
Efficient Training: Uses LoRA adapters for efficient parameter updates

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

Basic Inference

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "GetSoloTech/Gemma3-Code-Reasoning-4B"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Prepare input for competitive programming problem
messages = [
    {"role": "system", "content": "You are an expert competitive programmer. Read the problem and produce a correct, efficient solution. Include reasoning if helpful."},
    {"role": "user", "content": "Your programming problem here..."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate solution
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096,
    temperature=1.0,
    top_p=0.95,
    top_k=64
)

output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
print(content)

📈 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

🎛️ Recommended Settings

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

🔗 Related Resources

Base Model: gemma-3-4b-it
Training Dataset: Code-Reasoning
Training Framework: Unsloth
Original Dataset: OpenCodeReasoning-2

🤝 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

📞 Contact

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

Note: This model is specifically optimized for competitive programming and code reasoning tasks.

cublya/Gemma3-Code-Reasoning-4B

GetSoloTech/Gemma3-Code-Reasoning-4B