Instructions to use Salesforce/blip2-opt-2.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/blip2-opt-2.7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Salesforce/blip2-opt-2.7b")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") model = AutoModelForVisualQuestionAnswering.from_pretrained("Salesforce/blip2-opt-2.7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Salesforce/blip2-opt-2.7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Salesforce/blip2-opt-2.7b" # 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-2.7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Salesforce/blip2-opt-2.7b
- SGLang
How to use Salesforce/blip2-opt-2.7b 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-2.7b" \ --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-2.7b", "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-2.7b" \ --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-2.7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Salesforce/blip2-opt-2.7b with Docker Model Runner:
docker model run hf.co/Salesforce/blip2-opt-2.7b
BLIP2 Always Gives `\n` as Output
I've literally copied and pasted the demo code:
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
All I get for an output is \n. I'm running: torch==2.0.1+cu117 transformers==4.33.1.
original BLIP works well, as for other VQA model architectures, any ideas what I'm doing wrong?
I think there's an issue with the example. Once I looked further in the documentation I saw there's a specific prompt format:
qtext = f"Question: {question} Answer:"
If I send qtext I get good results.
Hi,
Yes the authors are aware of this, they explicitly strip() the output as seen here: https://github.com/salesforce/LAVIS/blob/e4040b13d6120062829ee9625f016f3cd3dd16e6/lavis/models/blip2_models/blip2_opt.py#L278.
Will update the model card, thanks for reporting.
Not updated yet ;) But I found this discussion fortunately :)
It still has not been fixed yet.
hello,i have the same promblem,has it been solved?