Instructions to use ys-zong/llava-v1.5-7b-Posthoc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ys-zong/llava-v1.5-7b-Posthoc with Transformers:

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

pipe = pipeline("text-generation", model="ys-zong/llava-v1.5-7b-Posthoc")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("ys-zong/llava-v1.5-7b-Posthoc")
model = AutoModelForCausalLM.from_pretrained("ys-zong/llava-v1.5-7b-Posthoc")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ys-zong/llava-v1.5-7b-Posthoc with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ys-zong/llava-v1.5-7b-Posthoc"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ys-zong/llava-v1.5-7b-Posthoc",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ys-zong/llava-v1.5-7b-Posthoc

SGLang

How to use ys-zong/llava-v1.5-7b-Posthoc 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 "ys-zong/llava-v1.5-7b-Posthoc" \
    --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": "ys-zong/llava-v1.5-7b-Posthoc",
		"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 "ys-zong/llava-v1.5-7b-Posthoc" \
        --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": "ys-zong/llava-v1.5-7b-Posthoc",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ys-zong/llava-v1.5-7b-Posthoc with Docker Model Runner:
```
docker model run hf.co/ys-zong/llava-v1.5-7b-Posthoc
```

VLGuard

[Website] [Paper] [Code]

Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models. (ICML 2024)

Weights

This is the model weights for LLaVA-v1.5-7B Posthoc Fine-tuning. You can use them in exactly the same way as the original LLaVA.

Usage

Please refer to Github for detailed usage.

Downloads last month: 21

Collection including ys-zong/llava-v1.5-7b-Posthoc

VLGuard

Collection

Data and Model weights for VLGuard: https://ys-zong.github.io/VLGuard/ • 13 items • Updated Jun 18, 2024 • 1

Paper for ys-zong/llava-v1.5-7b-Posthoc

Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models

Paper • 2402.02207 • Published Feb 3, 2024 • 2