Update README.md

README.md

@@ -35,11 +35,26 @@ This allows the model to be used for tasks like:
 - visual question answering (VQA)
 - chat-like conversations by feeding the image and the previous conversation as prompt to the model
 
-## Intended uses & limitations
+## Direct Use and Downstream Use
 
 You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for
 fine-tuned versions on a task that interests you.
 
+## Bias, Risks, Limitations, and Ethical Considerations
+
+BLIP2-OPT uses off-the-shelf OPT as the language model. It inherits the same risks and limitations as mentioned in Meta's model card.
+
+> Like other large language models for which the diversity (or lack thereof) of training
+> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
+> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
+> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
+> large language models.
+> 
+BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet.  As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.
+
+BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within.
+
+
 ### How to use
 
 For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example).