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# **Model Summary: Mify-Coder-2.5B**

## **Overview**
Mify-Coder-2.5B-8K 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.

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## **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.  

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## **Performance Highlights**

| **Category**   | **Benchmark**       | **# Shots** | **Metric** | **Scores**   |
|----------------|----------------------|-------------|------------|-------------------|
| Code Gen       | MBPP                | 0           | pass@1     | 89.23%     |
| 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%      |
| Tool Use       | BFCL v1             | 0           | acc        | 79.19%     |
| Tool Use       | BFCL v2             | 0           | acc        | 55.26%      |


- Outperforms larger models on algorithmic reasoning tasks while maintaining competitive general coding and security-oriented capabilities.

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## **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.

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## **Deployment & Future Work**
- **Quantization:** FP8 and AWQ for efficient inference; optimized with TensorRT-LLM.