Update README.md

README.md

@@ -5,7 +5,7 @@
 # **Model Summary: Mify-Coder-2.5B**
 
 ## **Overview**
-Mify-Coder-2.5B-v1 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.
+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.
 
@@ -15,7 +15,7 @@ Mify-Coder-2.5B-v1 is a **2.5B-parameter code-focused language model**. It deliv
 - **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.  
+  - **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.  
@@ -24,29 +24,27 @@ Mify-Coder-2.5B-v1 is a **2.5B-parameter code-focused language model**. It deliv
 
 ## **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           | acc        | 55.26%      |
-| Safety         | AIR-Bench                            | 0           | pass@1     | 67.32%     |
-| SecCode Gen    | CybersecEval4-Autocomplete           | 0           | pass@1     | 78.91%      |
-
-
-- Outperforms larger models on algorithmic reasoning tasks while maintaining competitive general coding and security-oriented capabilities.
+| **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%      |
 
 ---
 
 ## **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.
+- Validated against **Stanford AIR-Bench** and **CybersecEval4-Autocomplete** benchmarks.
 
 ---
 
 ## **Deployment & Future Work**
-- **Quantization:** FP8 and AWQ for efficient inference; optimized with TensorRT-LLM.
+- **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.