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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. |