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+# Sheikh-2.5-Coder
+
+**A lightweight 3B parameter code-focused language model inspired by MiniMax-M2 architecture, optimized for efficient on-device deployment.**
+
+## Model Description
+
+Sheikh-2.5-Coder is a 3 billion parameter transformer model specifically designed for code generation and programming assistance. Inspired by the efficient architecture of MiniMax-M2, this model delivers strong performance in code generation while being optimized for on-device deployment.
+
+### Key Features
+
+- **3B Parameters**: Optimized for efficiency and performance balance
+- **Code-Focused Training**: Trained on diverse programming languages and code patterns
+- **On-Device Ready**: Quantized variants available for mobile and edge deployment
+- **Multi-Language Support**: Handles multiple programming languages
+- **Chat Capabilities**: Instruction-tuned for conversational coding assistance
+- **Efficient Architecture**: Inspired by MiniMax-M2's efficiency principles
+
+### Performance Highlights
+
+- Competitive performance with models 2.5x larger
+- Optimized memory usage for mobile deployment
+- Fast inference times suitable for real-time applications
+- Strong performance on code generation benchmarks
+
+## Model Variants
+
+- **Base Model**: Full precision for research and development
+- **8-bit Quantized**: Balanced performance and memory usage
+- **4-bit Quantized**: Maximum efficiency for edge devices
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Basic Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "your-username/sheikh-2.5-coder"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Generate code
+prompt = "Write a function to calculate the factorial of a number:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.1)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Chat Usage
+
+```python
+# For conversational interaction
+messages = [
+    {"role": "user", "content": "Help me write a Python function to sort a list"}
+]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+outputs = model.generate(inputs, max_new_tokens=200, temperature=0.1)
+response = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
+print(response)
+```
+
+## Technical Specifications
+
+- **Parameters**: 3.09B (2.77B non-embedding)
+- **Context Length**: 32,768 tokens
+- **Architecture**: Transformer with attention optimizations
+- **Training Data**: Diverse programming languages and code-comment pairs
+- **Optimization**: Quantization-ready for on-device deployment
+
+## Benchmarks
+
+*Performance metrics will be added after training completion*
+
+## Deployment
+
+### CPU Inference
+
+```python
+model = AutoModelForCausalLM.from_pretrained(
+    "your-username/sheikh-2.5-coder",
+    torch_dtype=torch.float32,
+    device_map="cpu"
+)
+```
+
+### Mobile Deployment
+
+For mobile deployment, use the quantized variants:
+- 8-bit quantized model for balance of speed and accuracy
+- 4-bit quantized model for maximum efficiency
+
+## License
+
+[License information to be added]
+
+## Contributing
+
+We welcome contributions! Please see our contributing guidelines for more details.
+
+## Citation
+
+```bibtex
+@article{sheikh2024sheikh25coder,
+  title={Sheikh-2.5-Coder: Efficient On-Device Code Generation Model},
+  author={Author Name},
+  year={2024}
+}
+```
+
+## Acknowledgments
+
+- Inspired by MiniMax-M2 architecture
+- Trained on diverse code datasets
+- Built with modern transformer optimizations
+
+---
+
+**Note**: This is a research model. For production use, please thoroughly test performance and consider safety implications.