README.md

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

## **Overview**
Mify-Coder-2.5B-v1 is a breakthrough 2.5B-parameter code model fully designed, engineered, and trained at Infosys on 4.2T tokens on Mify-2.5B base model. Despite its compact size, Mify-Coder-2.5B-v1 sets a new benchmark for small language models, achieving performance parity with frontier open-source models in code generation and tool calling, along with exemplary performance on safety metrics in helpfulness and harmlessness, and superior throughput that surpasses larger frontier models.

**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, 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       | 91.21%     |
| Code Gen       | MBPP+                                | 0           | pass@1       | 89.15%      |
| Code Gen       | HumanEval                            | 0           | pass@1       | 53.66%      |
| Code Gen       | HumanEval+                           | 0           | pass@1       | 48.78%     |
| Code Gen       | NumpyEval                            | 0           | pass@1       | 56.44%     |
| Code Gen       | PandasEval                           | 0           | pass@1       | 53.47%      |
| Tool Use       | BFCL v2                              | 0           | overall acc  | 55.26%      |
| Safety         | AIR-Bench                            | 0           | pass@1       | 67.32%     |
| SecCode Gen    | CybersecEval4-Autocomplete           | 0           | pass@1       | 78.91%      |

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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 AIR-Bench** and **CybersecEval4-Autocomplete** benchmarks.

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## **Deployment & Future Work**
- **Quantization:** The model was optimized for low latency outperforming most sub-8B SLM models. Furthermore, the quantized variants of Mify-Coder can be seamlessly deployed and inferenced on standard desktop environments, eliminating the need for specialized hardware such as GPUs.
- Future work includes enhancing Mify-Coder with agentic coding competencies and scaling its context length. The model weights will be open-sourced early next year to accelerate research and real-world deployment.