Instructions to use ammag/image-caption-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ammag/image-caption-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ammag/image-caption-test")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ammag/image-caption-test") model = AutoModelForMultimodalLM.from_pretrained("ammag/image-caption-test") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ammag/image-caption-test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ammag/image-caption-test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ammag/image-caption-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ammag/image-caption-test
- SGLang
How to use ammag/image-caption-test 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 "ammag/image-caption-test" \ --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": "ammag/image-caption-test", "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 "ammag/image-caption-test" \ --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": "ammag/image-caption-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ammag/image-caption-test with Docker Model Runner:
docker model run hf.co/ammag/image-caption-test
| { | |
| "_name_or_path": "Salesforce/blip-image-captioning-base", | |
| "architectures": [ | |
| "BlipForConditionalGeneration" | |
| ], | |
| "image_text_hidden_size": 256, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "logit_scale_init_value": 2.6592, | |
| "model_type": "blip", | |
| "projection_dim": 512, | |
| "text_config": { | |
| "initializer_factor": 1.0, | |
| "model_type": "blip_text_model", | |
| "num_attention_heads": 12 | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.32.0.dev0", | |
| "vision_config": { | |
| "dropout": 0.0, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "model_type": "blip_vision_model", | |
| "num_channels": 3 | |
| } | |
| } | |