Instructions to use YanweiLi/llama-vid-13b-pretrain-336 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YanweiLi/llama-vid-13b-pretrain-336 with Transformers:

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

pipe = pipeline("text-generation", model="YanweiLi/llama-vid-13b-pretrain-336")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("YanweiLi/llama-vid-13b-pretrain-336")
model = AutoModelForCausalLM.from_pretrained("YanweiLi/llama-vid-13b-pretrain-336", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YanweiLi/llama-vid-13b-pretrain-336 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YanweiLi/llama-vid-13b-pretrain-336"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YanweiLi/llama-vid-13b-pretrain-336",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/YanweiLi/llama-vid-13b-pretrain-336

SGLang

How to use YanweiLi/llama-vid-13b-pretrain-336 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 "YanweiLi/llama-vid-13b-pretrain-336" \
    --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": "YanweiLi/llama-vid-13b-pretrain-336",
		"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 "YanweiLi/llama-vid-13b-pretrain-336" \
        --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": "YanweiLi/llama-vid-13b-pretrain-336",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use YanweiLi/llama-vid-13b-pretrain-336 with Docker Model Runner:
```
docker model run hf.co/YanweiLi/llama-vid-13b-pretrain-336
```

YanweiLi commited on Dec 3, 2023

Commit

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1 Parent(s): a7c29f2

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+---
+tags:
+- vision-language model
+- llama
+- video understanding
+---
+# LLaMA-VID Model Card
+<a href='https://llama-vid.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
+<a href='https://arxiv.org/abs/2311.17043'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
+## Model details
+LLaMA-VID empowers existing frameworks to support hour-long videos and pushes their upper limit with an extra context token.
+**Model type:**
+LLaMA-VID is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
+LLaMA-VID empowers existing frameworks to support hour-long videos and pushes their upper limit with an extra context token. We build this repo based on LLaVA.
+**Model date:**
+llama-vid-13b-pretrain-336 was trained on 11/2023.
+## License
+Llama 2 is licensed under the LLAMA 2 Community License,
+Copyright (c) Meta Platforms, Inc. All Rights Reserved.
+**Where to send questions or comments about the model:**
+https://github.com/dvlab-research/LLaMA-VID/issues
+## Intended use
+**Primary intended uses:**
+The primary use of LLaMA-VID is research on large multimodal models and chatbots.
+**Primary intended users:**
+The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
+## Training data
+This model is trained based on LLaVA-1.5 dataset, including
+- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.