Update README.md

README.md

@@ -27,7 +27,7 @@ Unlike traditional multi-stage pipelines that are prone to cascading error propa
 ## 🚀 Model Highlights
 - **Joint Extraction:** Unified NER + RE reducing pipeline complexity.
 - **Ontology-Adaptive:** Zero-shot adaptation to diverse domains (Astronomy, Music, Healthcare, etc.) via dynamic schema variables.
-- **Local & Private:** Optimized for **local CPU-only inference** (via GGUF/Ollama), ensuring data sovereignty without external API dependencies.
+- **Local & Private:** Optimized for **local CPU-only inference** (via GGUF/Ollama - FinaPolat/phi4_adaptableIE_v2-gguf ), ensuring data sovereignty without external API dependencies.
 - **Instruction Aligned:** Fine-tuned to follow strict negative constraints, ensuring zero conversational filler in outputs.
 
 ## 🛠 Methodology
@@ -112,9 +112,11 @@ outputs = llm.chat(batch_prompts, sampling_params=sampling_params, use_tqdm=True
 
 ### 4. 📦 Deployment & Hardware Requirements
 
-Deployment Mode QuantizationHardware RequirementTarget 
-LatencyServer-sideBF161x NVIDIA A100/RTX 4090 (24GB+)Ultra-Low
-Local Consumer4-bit GGUF16GB RAM (Apple Silicon / PC CPU)Moderate
+| Deployment Mode | Quantization | Hardware Requirement                     | Target Latency |
+|-----------------|--------------|------------------------------------------|----------------|
+| Server-side     | BF16         | 1× NVIDIA A100 / RTX 4090 (24GB+)         | Ultra-Low      |
+| Local Consumer  | 4-bit GGUF   | 16GB RAM (Apple Silicon / PC CPU)         | Moderate       |
+
 
 For CPU-only local execution, refer to the GGUF version: phi4_adaptableIE_v2-gguf📜