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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# **Model Summary: Mify-Coder-2.5B**
+
+## **Overview**
+Mify-Coder-2.5B-v0.1 is a **2.5B-parameter code-focused language model**. It delivers **frontier-grade performance** in code generation, reasoning, and function calling tasks while maintaining **compute efficiency and enterprise-grade safety**. Unlike scale-first paradigms, Mify-Coder demonstrates that smaller models can achieve competitive results through principled data curation and optimized training strategies.
+
+**Developed by**: Infosys Ltd.
+
+---
+
+## **Architecture & Training**
+- **Base Model:** Mify-2.5B  
+- **Training Phases:**  
+  - **Continual Pretraining (CPT):** Next-token prediction with Fill-in-the-Middle (FIM) for structural infilling.  
+  - **Supervised Fine-Tuning (SFT):** Instruction alignment for coding tasks, multi-turn dialogues, function calling, and safety.  
+- **Optimization:**  
+  - **BF16 mixed precision**, **Grouped Query Attention (GQA)**, and **Distributed Fused Adam** optimizer.  
+  - Specialized tokenization with syntax markers and reasoning tokens for advanced behaviors.  
+
+---
+
+## **Performance Highlights**
+
+| **Category**   | **Benchmark**       | **# Shots** | **Metric** | **Scores**   |
+|----------------|----------------------|-------------|------------|-------------------|
+| Code Gen       | MBPP                | 0           | pass@1     | 90.70%     |
+| Code Gen       | MBPP+               | 0           | pass@1     | 88.89%      |
+| Code Gen       | HumanEval           | 0           | pass@1     | 53.05%      |
+| Code Gen       | HumanEval+          | 0           | pass@1     | 46.95%     |
+| Code Gen       | NumpyEval           | 0           | pass@1     | 56.44%     |
+| Code Gen       | PandasEval          | 0           | pass@1     | 53.47%      |
+
+- Outperforms larger models on algorithmic reasoning tasks while maintaining competitive general coding and security-oriented capabilities.
+
+---
+
+## **Responsible AI & Safety**
+- Integrated safety objectives during SFT.  
+- Balanced harmful/general sample ratio (1:4) for secure code generation and ethical language use.  
+- Validated against **Stanford AirBench** and **CyberSecEval** benchmarks.
+
+---
+
+## **Deployment & Future Work**
+- **Quantization:** FP8 and AWQ for efficient inference; optimized with TensorRT-LLM.  
+
+
+