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

98be0af

1 Parent(s): ec9fb99

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README.md +6 -6
README_zh.md +4 -4

README.md CHANGED Viewed

@@ -4,12 +4,12 @@ license_name: cogvlm2
 license_link: https://huggingface.co/THUDM/cogvlm2-video-llama3-base/blob/main/LICENSE
 language:
-- en
 pipeline_tag: text-generation
 tags:
-- chat
-- cogvlm2
-- cogvlm--video
 inference: false
 ---
@@ -99,9 +99,9 @@ our [github](https://github.com/THUDM/CogVLM2/tree/main/video_demo).
 ## License
 This model is released under the
-CogVLM2  [LICENSE](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-base/file/view/master?fileName=LICENSE&status=0).
 For models built with Meta Llama 3, please also adhere to
-the [LLAMA3_LICENSE](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-base/file/view/master?fileName=LLAMA3_LICENSE&status=0).
 ## Training details

 license_link: https://huggingface.co/THUDM/cogvlm2-video-llama3-base/blob/main/LICENSE
 language:
+  - en
 pipeline_tag: text-generation
 tags:
+  - chat
+  - cogvlm2
+  - cogvlm--video
 inference: false
 ---
 ## License
 This model is released under the
+CogVLM2  [LICENSE](./LICENSE).
 For models built with Meta Llama 3, please also adhere to
+the [LLAMA3_LICENSE](./LLAMA3_LICENSE).
 ## Training details

README_zh.md CHANGED Viewed

@@ -2,8 +2,8 @@
 ## 介绍
-CogVLM2-Video 在多个视频问答任务上实现了最先进的性能。能够实现一分钟内的视频理解。 我们提供了两个示例视频，分别展现了
-CogVLM2-Video 的 视频理解和时间序列定位能力。
 <table>
   <tr>
     <td>
@@ -77,9 +77,9 @@ prompt = f"The input consists of a sequence of key frames from a video. Answer t
 ## 模型协议
 此模型根据
-CogVLM2 [LICENSE](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-base/file/view/master?fileName=LICENSE&status=0)
 发布。对于使用 Meta Llama 3 构建的模型，还请遵守
-[LLAMA3_LICENSE](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-base/file/view/master?fileName=LLAMA3_LICENSE&status=0)。
 ## 引用

 ## 介绍
+CogVLM2-Video 在多个视频问答任务上达到了 state-of-the-art 的性能，能够实现一分钟内的视频理解。
+我们提供了两个示例视频，分别展现了 CogVLM2-Video 的 视频理解和时间序列定位能力。
 <table>
   <tr>
     <td>
 ## 模型协议
 此模型根据
+CogVLM2 [LICENSE](./LICENSE)
 发布。对于使用 Meta Llama 3 构建的模型，还请遵守
+[LLAMA3_LICENSE](./LLAMA3_LICENSE)。
 ## 引用