Instructions to use zai-org/cogagent-vqa-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/cogagent-vqa-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/cogagent-vqa-hf", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("zai-org/cogagent-vqa-hf", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zai-org/cogagent-vqa-hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/cogagent-vqa-hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/cogagent-vqa-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zai-org/cogagent-vqa-hf
- SGLang
How to use zai-org/cogagent-vqa-hf 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 "zai-org/cogagent-vqa-hf" \ --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": "zai-org/cogagent-vqa-hf", "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 "zai-org/cogagent-vqa-hf" \ --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": "zai-org/cogagent-vqa-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zai-org/cogagent-vqa-hf with Docker Model Runner:
docker model run hf.co/zai-org/cogagent-vqa-hf
how to perform inference over multi-gpu setup
#2
by fcakyon - opened
as given in the readme of https://huggingface.co/THUDM/cogvlm-chat-hf
device_map = infer_auto_device_map(model, max_memory={0:'20GiB',1:'20GiB','cpu':'16GiB'}, no_split_module_classes=['CogVLMDecoderLayer', 'TransformerLayer'])
how to dispatch THUDM/cogagent-vqa-hf model into multiple gpus?
cc: @qingsonglv @chenkq
I managed to perform inference on multiple gpus also by following example from https://huggingface.co/THUDM/cogvlm-chat-hf and replacing device_map with:
device_map = infer_auto_device_map(model, max_memory={0:'18GiB',1:'18GiB','cpu':'16GiB'},no_split_module_classes=['CogAgentDecoderLayer'])