Upload AWQ 4-bit quantized Molmo-7B-D (~5.2GB, 63.0% reduction)

README.md

@@ -36,7 +36,7 @@ This is a 4-bit AWQ quantized version of [allenai/Molmo-7B-D-0924](https://huggi
 - **Architecture:** Molmo (Qwen2-7B decoder + OpenAI CLIP vision encoder)
 - **Quantization Method:** AWQ (Activation-aware Weight Quantization)
 - **Quantization Scheme:** W4A16 (4-bit weights, 16-bit activations)
-- **Calibration Dataset:** Flickr30k (32 samples)
+- **Calibration Dataset:** Flickr30k (128 samples)
 
 ## Size Comparison
 
@@ -150,8 +150,8 @@ Molmo-7B-D is part of the Molmo family of open vision-language models developed
 
 - **Method:** AWQ (Activation-aware Weight Quantization)
 - **Independent Pipeline:** Used with BasicPipeline for layer-by-layer quantization
-- **Calibration:** 32 Flickr30k image-text pairs
-- **Max Sequence Length:** 1024 tokens
+- **Calibration:** 128 Flickr30k image-text pairs
+- **Max Sequence Length:** 2048 tokens
 - **Why AWQ**: Activation-aware quantization preserves important weights
 
 ## Limitations