Instructions to use lmms-lab/LLaVA-OneVision-1.5-4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmms-lab/LLaVA-OneVision-1.5-4B-Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="lmms-lab/LLaVA-OneVision-1.5-4B-Instruct")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lmms-lab/LLaVA-OneVision-1.5-4B-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lmms-lab/LLaVA-OneVision-1.5-4B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmms-lab/LLaVA-OneVision-1.5-4B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmms-lab/LLaVA-OneVision-1.5-4B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lmms-lab/LLaVA-OneVision-1.5-4B-Instruct

SGLang

How to use lmms-lab/LLaVA-OneVision-1.5-4B-Instruct 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 "lmms-lab/LLaVA-OneVision-1.5-4B-Instruct" \
    --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": "lmms-lab/LLaVA-OneVision-1.5-4B-Instruct",
		"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 "lmms-lab/LLaVA-OneVision-1.5-4B-Instruct" \
        --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": "lmms-lab/LLaVA-OneVision-1.5-4B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lmms-lab/LLaVA-OneVision-1.5-4B-Instruct with Docker Model Runner:
```
docker model run hf.co/lmms-lab/LLaVA-OneVision-1.5-4B-Instruct
```

Improve model card: add prominent links, comprehensive guides, and project details

by nielsr HF Staff - opened Oct 5, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+303

-45

nielsr

Oct 5, 2025

This PR significantly enhances the model card for LLaVA-OneVision-1.5 by:

Adding prominent links to the paper, GitHub code repository, Hugging Face collection (as project page), and Hugging Face Space (as demo) directly below the title, making essential resources easily discoverable.
Removing redundant sections, specifically the old "

Improve model card: add prominent links, comprehensive guides, and project detailsff264893

xiangan changed pull request status to merged Oct 5, 2025

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