Instructions to use liuhaotian/llava-v1.5-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liuhaotian/llava-v1.5-13b with Transformers:

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

pipe = pipeline("image-text-to-text", model="liuhaotian/llava-v1.5-13b")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("liuhaotian/llava-v1.5-13b")
model = AutoModelForCausalLM.from_pretrained("liuhaotian/llava-v1.5-13b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use liuhaotian/llava-v1.5-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liuhaotian/llava-v1.5-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liuhaotian/llava-v1.5-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/liuhaotian/llava-v1.5-13b

SGLang

How to use liuhaotian/llava-v1.5-13b 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 "liuhaotian/llava-v1.5-13b" \
    --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": "liuhaotian/llava-v1.5-13b",
		"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 "liuhaotian/llava-v1.5-13b" \
        --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": "liuhaotian/llava-v1.5-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use liuhaotian/llava-v1.5-13b with Docker Model Runner:
```
docker model run hf.co/liuhaotian/llava-v1.5-13b
```

llava-v1.5-13b / README.md

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	---
	inference: false
	---

	<br>
	<br>

	# LLaVA Model Card

	## Model details

	Model type:
	LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
	It is an auto-regressive language model, based on the transformer architecture.

	Model date:
	LLaVA-v1.5-13B was trained in September 2023.

	Paper or resources for more information:
	https://llava-vl.github.io/

	## 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/haotian-liu/LLaVA/issues

	## Intended use
	Primary intended uses:
	The primary use of LLaVA 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 dataset
	- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
	- 158K GPT-generated multimodal instruction-following data.
	- 450K academic-task-oriented VQA data mixture.
	- 40K ShareGPT data.

	## Evaluation dataset
	A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.