Update README.md

README.md

@@ -7,21 +7,30 @@ inference: False
 ---
 ## Sharded BLIP-2 Model Card - flan-t5-xl
 
-This is a sharded version of the [blip2-flan-t5-xl](https://huggingface.co/models/Salesforce/blip2-flan-t5-xl) which leverages [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) for image-to-text tasks such as image captioning and visual question answering.
+This is a sharded version of the [blip2-flan-t5-xl](https://huggingface.co/Salesforce/blip2-flan-t5-xl) which leverages [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) for image-to-text tasks such as image captioning and visual question answering.
 
-Refer to the [original model card](https://huggingface.co/models/Salesforce/blip2-flan-t5-xl) for more details about the model description, intended uses, and limitations, as well as instructions for how to use the model on CPU and GPU in different precisions.
+- this model repo is sharded so it can be easily loaded on low-RAM Colab runtimes :)
+- Refer to the [original model card](https://huggingface.co/Salesforce/blip2-flan-t5-xl) for more details about the model description, intended uses, and limitations, as well as instructions for how to use the model on CPU and GPU in different precisions.
 
 ## Usage
 
 Refer to the original model card for details or see [this blog post](https://huggingface.co/blog/blip-2#using-blip-2-with-hugging-face-transformers). Here is how you can use it on CPU:
 
+Install
+
+Requires the current `main` of transformers (_at time of writing_):
+```bash
+pip install accelerate git+https://github.com/huggingface/transformers.git -U -q
+```
+
+Use (_this is for CPU, check out the original model card/blog for `fp16` and `int8` usage_)
 
 ```python
 import requests
 from PIL import Image
 from transformers import BlipProcessor, Blip2ForConditionalGeneration
 
-model_name = "Salesforce/blip2-flan-t5-xl")
+model_name = "ethzanalytics/blip2-flan-t5-xl-sharded"
 processor = BlipProcessor.from_pretrained(model_name)
 model = Blip2ForConditionalGeneration.from_pretrained(model_name)