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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. |