Instructions to use teowu/llava_v1.5_7b_qinstruct_preview_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teowu/llava_v1.5_7b_qinstruct_preview_v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="teowu/llava_v1.5_7b_qinstruct_preview_v0.1")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("teowu/llava_v1.5_7b_qinstruct_preview_v0.1")
model = AutoModelForCausalLM.from_pretrained("teowu/llava_v1.5_7b_qinstruct_preview_v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use teowu/llava_v1.5_7b_qinstruct_preview_v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teowu/llava_v1.5_7b_qinstruct_preview_v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teowu/llava_v1.5_7b_qinstruct_preview_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/teowu/llava_v1.5_7b_qinstruct_preview_v0.1

SGLang

How to use teowu/llava_v1.5_7b_qinstruct_preview_v0.1 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 "teowu/llava_v1.5_7b_qinstruct_preview_v0.1" \
    --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": "teowu/llava_v1.5_7b_qinstruct_preview_v0.1",
		"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 "teowu/llava_v1.5_7b_qinstruct_preview_v0.1" \
        --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": "teowu/llava_v1.5_7b_qinstruct_preview_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use teowu/llava_v1.5_7b_qinstruct_preview_v0.1 with Docker Model Runner:
```
docker model run hf.co/teowu/llava_v1.5_7b_qinstruct_preview_v0.1
```

This is a preview version of the Q-Instruct LLaVA. Non-finalized weights.

@misc{wu2023qinstruct,
      title={Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models}, 
      author={Haoning Wu and Zicheng Zhang and Erli Zhang and Chaofeng Chen and Liang Liao and Annan Wang and Kaixin Xu and Chunyi Li and Jingwen Hou and Guangtao Zhai and Geng Xue and Wenxiu Sun and Qiong Yan and Weisi Lin},
      year={2023},
      eprint={2311.06783},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

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Paper for teowu/llava_v1.5_7b_qinstruct_preview_v0.1

Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

Paper • 2311.06783 • Published Nov 12, 2023 • 28