Instructions to use adept/fuyu-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adept/fuyu-8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="adept/fuyu-8b")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("adept/fuyu-8b") model = AutoModelForImageTextToText.from_pretrained("adept/fuyu-8b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use adept/fuyu-8b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "adept/fuyu-8b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adept/fuyu-8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/adept/fuyu-8b
- SGLang
How to use adept/fuyu-8b 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 "adept/fuyu-8b" \ --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": "adept/fuyu-8b", "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 "adept/fuyu-8b" \ --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": "adept/fuyu-8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use adept/fuyu-8b with Docker Model Runner:
docker model run hf.co/adept/fuyu-8b
Performance Sharing
For the two examples in the README, it seems to be cherry-picked.
Actually using the same chart and change to a more general question, it is non-sensing.
text_prompt = "What is this chart demostrating?\n"
Result: The chart demonstrates that life expectancy at birth has increased steadily over the years. In 2008, life expectancy at birth was 78.2 years, and 80.2 years was reached in 2017. The graph shows a steady upward trend, indicating a continued improvement in life expectancy. The graph also shows that life expectancy at birth has expanded significantly between 2008 and 2017, from 80.2 years to 80.5 years.
So, from my perspective, there is a huge space for improvement and actually cannot put into real use.
text_prompt = "What is this screenshot about?\n"
Result: The screenshot displays a message on a cell phone, which reads "New Vision-Language Model: BakLaVa-1, finetuned on Mistral 7B." The message appears to be from a person who is discussing a new model. The message also hints at a new message, which appears to be from another person. The messages are accompanied by two icons, one on the left and another on the right side of the screen.
Hey @Starlento , the prompt structure for that base model does seem to matter, as the model was trained on particular prompts. Prepending a specific prompt with "Answer the following DocVQA question based on the image." seems to do better for instance.