Instructions to use LanguageBind/Video-LLaVA-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LanguageBind/Video-LLaVA-7B with Transformers:

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

pipe = pipeline("text-generation", model="LanguageBind/Video-LLaVA-7B")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("LanguageBind/Video-LLaVA-7B")
model = AutoModelForCausalLM.from_pretrained("LanguageBind/Video-LLaVA-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LanguageBind/Video-LLaVA-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LanguageBind/Video-LLaVA-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LanguageBind/Video-LLaVA-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LanguageBind/Video-LLaVA-7B

SGLang

How to use LanguageBind/Video-LLaVA-7B 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 "LanguageBind/Video-LLaVA-7B" \
    --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": "LanguageBind/Video-LLaVA-7B",
		"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 "LanguageBind/Video-LLaVA-7B" \
        --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": "LanguageBind/Video-LLaVA-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LanguageBind/Video-LLaVA-7B with Docker Model Runner:
```
docker model run hf.co/LanguageBind/Video-LLaVA-7B
```

Add new task tag

#11

by merve HF Staff - opened Jul 13, 2024

base: refs/heads/main

←

from: refs/pr/11

Discussion Files changed

-3

Files changed (1) hide show

README.md +2 -3

README.md CHANGED Viewed

@@ -1,5 +1,6 @@
 ---
 license: apache-2.0
 ---
@@ -233,6 +234,4 @@ If you find our paper and code useful in your research, please consider giving a
 <a href="https://github.com/PKU-YuanGroup/Video-LLaVA/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=PKU-YuanGroup/Video-LLaVA" />
-</a>

 ---
 license: apache-2.0
+pipeline_tag: video-text-to-text
 ---
 <a href="https://github.com/PKU-YuanGroup/Video-LLaVA/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=PKU-YuanGroup/Video-LLaVA" />
+</a>