Update README.md

README.md

@@ -30,9 +30,9 @@ This is an INT4 quantized version of [SmolLM3-3B](https://huggingface.co/Hugging
 
 ## Model Overview
 
-- **Base Model:** SmolLM3-3B (3B parameters)
+- **Base Model:** SmolLM3-3B
 - **Quantization:** INT4 via OpenVINO
-- **Size Reduction:** ~75% smaller than original
+- **Size Reduction:** Significant compression achieved
 - **Target Hardware:** CPUs, Intel GPUs, NPUs
 - **Use Cases:** Local inference, edge deployment, resource-constrained environments
 
@@ -55,10 +55,13 @@ This is an INT4 quantized version of [SmolLM3-3B](https://huggingface.co/Hugging
 
 > ⚠️ **Note:** This is an experimental quantization. Formal benchmarks pending.
 
-Expected characteristics:
-- **Model Size:** ~1GB (vs ~6GB fp16)
-- **Inference Speed:** 2-4x faster on CPU
-- **Quality Trade-off:** Minor degradation expected
+Expected benefits of INT4 quantization:
+- Reduced model size
+- Faster CPU inference
+- Lower memory requirements
+- Some quality trade-off
+
+Actual metrics will be added after proper benchmarking.
 
 ## 🛠️ How to Use
 
@@ -108,8 +111,8 @@ text = tokenizer.apply_chat_template(
 ## ⚡ Optimization Tips
 
 1. **CPU Inference:** Use OpenVINO runtime for best performance
-2. **Batch Processing:** Leverage dynamic batching when possible
-3. **Memory:** Requires ~2GB RAM for comfortable operation
+2. **Batch Processing:** Consider batching requests when possible
+3. **Memory:** INT4 significantly reduces memory requirements
 
 ## 🧪 Experimental Status