Instructions to use Salesforce/blip2-opt-6.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Salesforce/blip2-opt-6.7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Salesforce/blip2-opt-6.7b")

# Load model directly
from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering

processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-6.7b")
model = AutoModelForVisualQuestionAnswering.from_pretrained("Salesforce/blip2-opt-6.7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Salesforce/blip2-opt-6.7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Salesforce/blip2-opt-6.7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Salesforce/blip2-opt-6.7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Salesforce/blip2-opt-6.7b

SGLang

How to use Salesforce/blip2-opt-6.7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Salesforce/blip2-opt-6.7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Salesforce/blip2-opt-6.7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Salesforce/blip2-opt-6.7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Salesforce/blip2-opt-6.7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Salesforce/blip2-opt-6.7b with Docker Model Runner:
```
docker model run hf.co/Salesforce/blip2-opt-6.7b
```

JunnanLi commited on Mar 20, 2023

Commit

f998da1

1 Parent(s): 8e6e2ff

Update README.md

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@@ -36,11 +36,25 @@ 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
 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.
 ### 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).

 - visual question answering (VQA)
 - chat-like conversations by feeding the image and the previous conversation as prompt to the model
+## 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).