diff --git a/README.md b/README.md
index 7650a23..979a38a 100644
--- a/README.md
+++ b/README.md
@@ -26,7 +26,7 @@ Task | BLIP w/ ViT-B | BLIP w/ ViT-B and CapFilt-L | BLIP w/ ViT-L
 Image-Text Retrieval (COCO) | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth">Download</a>| - | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_coco.pth">Download</a>
 Image-Text Retrieval (Flickr30k) | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth">Download</a>|  - | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_flickr.pth">Download</a>
 Image Captioning (COCO) | - | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth">Download</a>| <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth">Download</a> | 
-VQA | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth">Download</a>| - | - 
+VQA | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth">Download</a>| <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth">Download</a> | - 
 NLVR2 | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth">Download</a>| - | - 
 
 
@@ -46,8 +46,22 @@ NLVR2 | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLI
 1. Download COCO and NoCaps datasets from the original websites, and set 'image_root' in configs/caption_coco.yaml and configs/nocaps.yaml accordingly.
 2. To evaluate the finetuned BLIP model on COCO, run:
 <pre>python -m torch.distributed.run --nproc_per_node=8 --use_env train_caption.py --evaluate</pre> 
-3. To evaluate the finetuned BLIP model on NoCaps, generate results with:
+3. To evaluate the finetuned BLIP model on NoCaps, generate results with: (evaluation needs to be performed on official server)
 <pre>python -m torch.distributed.run --nproc_per_node=8 --use_env eval_nocaps.py </pre> 
 4. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set 'pretrained' in configs/caption_coco.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base.pth". Then run:
 <pre>python -m torch.distributed.run --nproc_per_node=8 --use_env train_caption.py </pre> 
 
+### VQA:
+1. Download VQA v2 dataset and Visual Genome dataset from the original websites, and set 'vqa_root' and 'vg_root' in configs/vqa.yaml.
+2. To evaluate the finetuned BLIP model, generate results with: (evaluation needs to be performed on official server)
+<pre>python -m torch.distributed.run --nproc_per_node=8 --use_env train_vqa.py --evaluate</pre> 
+3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set 'pretrained' in configs/vqa.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base.pth". Then run:
+<pre>python -m torch.distributed.run --nproc_per_node=16 --use_env train_vqa.py </pre> 
+
+### NLVR2:
+1. Download NLVR2 dataset from the original websites, and set 'image_root' in configs/nlvr.yaml.
+2. To evaluate the finetuned BLIP model, run
+<pre>python -m torch.distributed.run --nproc_per_node=8 --use_env train_nlvr.py --evaluate</pre> 
+3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set 'pretrained' in configs/nlvr.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth". Then run:
+<pre>python -m torch.distributed.run --nproc_per_node=16 --use_env train_nlvr.py </pre> 
+