GetSoloTech/Gemma3-Code-Reasoning-4B
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
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.