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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.
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 | 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.
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.