Instructions to use zai-org/cogvlm2-video-llama3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/cogvlm2-video-llama3-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zai-org/cogvlm2-video-llama3-base", 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-video-llama3-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/cogvlm2-video-llama3-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/cogvlm2-video-llama3-base"
# 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-video-llama3-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/cogvlm2-video-llama3-base

SGLang

How to use zai-org/cogvlm2-video-llama3-base 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-video-llama3-base" \
    --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-video-llama3-base",
		"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-video-llama3-base" \
        --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-video-llama3-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/cogvlm2-video-llama3-base with Docker Model Runner:
```
docker model run hf.co/zai-org/cogvlm2-video-llama3-base
```

zR commited on Jul 7, 2024

Commit

ec9fb99

1 Parent(s): 2978dc0

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README.md +1 -1

README.md CHANGED Viewed

@@ -46,7 +46,7 @@ the [MVBench](https://github.com/OpenGVLab/Ask-Anything), [VideoChatGPT-Bench](h
 and Zero-shot VideoQA datasets (MSVD-QA, MSRVTT-QA, ActivityNet-QA). Where VCG-* refers to the VideoChatGPTBench, ZS-*
 refers to Zero-Shot VideoQA datasets and MV-* refers to main categories in the MVBench.
-![Quantitative Evaluation](https://github.com/THUDM/CogVLM2/tree/main/resources/cogvlm2_video_bench.jpeg)
 Performance on VideoChatGPT-Bench and Zero-shot VideoQA dataset:

 and Zero-shot VideoQA datasets (MSVD-QA, MSRVTT-QA, ActivityNet-QA). Where VCG-* refers to the VideoChatGPTBench, ZS-*
 refers to Zero-Shot VideoQA datasets and MV-* refers to main categories in the MVBench.
+![Quantitative Evaluation](https://raw.githubusercontent.com/THUDM/CogVLM2/main/resources/cogvlm2_video_bench.jpeg)
 Performance on VideoChatGPT-Bench and Zero-shot VideoQA dataset: