Create README.md

README.md

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+---
+datasets:
+- GetSoloTech/Code-Reasoning
+base_model:
+- google/gemma-3-4b-it
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code-generation
+- competitive-programming
+- code-reasoning
+- programming
+- algorithms
+- problem-solving
+---
+
+# 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](https://huggingface.co/datasets/GetSoloTech/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](https://huggingface.co/google/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](https://huggingface.co/google/gemma-3-4b-it)
+- **Training Dataset**: [Code-Reasoning](https://huggingface.co/datasets/GetSoloTech/Code-Reasoning)
+- **Training Framework**: [Unsloth](https://github.com/unslothai/unsloth)
+- **Original Dataset**: [OpenCodeReasoning-2](https://huggingface.co/datasets/nvidia/OpenCodeReasoning-2)
+
+## 🤝 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://docs.unsloth.ai/)
+
+
+## 🙏 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.