Image-Text-to-Text
Transformers
Safetensors
English
llava_qwen2
text-generation
multimodal
conversational
Instructions to use zhaode/FastVLM-7B-Stage3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zhaode/FastVLM-7B-Stage3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="zhaode/FastVLM-7B-Stage3") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("zhaode/FastVLM-7B-Stage3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zhaode/FastVLM-7B-Stage3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zhaode/FastVLM-7B-Stage3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zhaode/FastVLM-7B-Stage3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/zhaode/FastVLM-7B-Stage3
- SGLang
How to use zhaode/FastVLM-7B-Stage3 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 "zhaode/FastVLM-7B-Stage3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zhaode/FastVLM-7B-Stage3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "zhaode/FastVLM-7B-Stage3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zhaode/FastVLM-7B-Stage3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use zhaode/FastVLM-7B-Stage3 with Docker Model Runner:
docker model run hf.co/zhaode/FastVLM-7B-Stage3
Download llava/model/utils.py from zhaode/FastVLM-7B-Stage3: direct link, hf CLI and curl.
- Browser
- Download file 927 Bytes
-
https://huggingface.co/zhaode/FastVLM-7B-Stage3/resolve/main/llava/model/utils.py
- Command line
-
hf download hf://zhaode/FastVLM-7B-Stage3/llava/model/utils.py
-
curl -L -o utils.py https://huggingface.co/zhaode/FastVLM-7B-Stage3/resolve/main/llava/model/utils.py
927 Bytes
| from transformers import AutoConfig | |
| def auto_upgrade(config): | |
| cfg = AutoConfig.from_pretrained(config) | |
| if 'llava' in config and 'llava' not in cfg.model_type: | |
| assert cfg.model_type == 'llama' | |
| print("You are using newer LLaVA code base, while the checkpoint of v0 is from older code base.") | |
| print("You must upgrade the checkpoint to the new code base (this can be done automatically).") | |
| confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]") | |
| if confirm.lower() in ["y", "yes"]: | |
| print("Upgrading checkpoint...") | |
| assert len(cfg.architectures) == 1 | |
| setattr(cfg.__class__, "model_type", "llava") | |
| cfg.architectures[0] = 'LlavaLlamaForCausalLM' | |
| cfg.save_pretrained(config) | |
| print("Checkpoint upgraded.") | |
| else: | |
| print("Checkpoint upgrade aborted.") | |
| exit(1) | |