Instructions to use zai-org/cogvlm2-llama3-chat-19B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/cogvlm2-llama3-chat-19B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/cogvlm2-llama3-chat-19B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("zai-org/cogvlm2-llama3-chat-19B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zai-org/cogvlm2-llama3-chat-19B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/cogvlm2-llama3-chat-19B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/cogvlm2-llama3-chat-19B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zai-org/cogvlm2-llama3-chat-19B
- SGLang
How to use zai-org/cogvlm2-llama3-chat-19B 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/cogvlm2-llama3-chat-19B" \ --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": "zai-org/cogvlm2-llama3-chat-19B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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/cogvlm2-llama3-chat-19B" \ --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": "zai-org/cogvlm2-llama3-chat-19B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use zai-org/cogvlm2-llama3-chat-19B with Docker Model Runner:
docker model run hf.co/zai-org/cogvlm2-llama3-chat-19B
1-Click installers for Windows, RunPod, Massed Compute and ultra advanced CogVLM 2 Batch Processing Gradio APP with 4-bit quantization
Great work THUDM team thank for this amazing model
The app and the installers : https://www.patreon.com/posts/120193330
Check below screenshots to see how to use it
Currently the APP works amazing with 4-bit quantization very fast
I am searching to lower VRAM usage even further with like adding CPU-Offloading and other stuff if possible
Previously we were lacking Triton but it now works perfect
My installer installs into a Python 3.10 VENV completely isolated and clean
You can see entire APP and installer source code
If you get Triton error make sure to delete your Triton cache after installing the app like below
C:\Users\Furkan.triton
You can find 19 example captioning here very powerful : https://www.reddit.com/r/SECourses/comments/1i3i53q/most_powerful_vision_model_cogvlm_2_now_works/



