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

Libraries

How to use ys-zong/llava-v1.5-7b-Clean-lora 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-Clean-lora")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("ys-zong/llava-v1.5-7b-Clean-lora")
model = AutoModelForCausalLM.from_pretrained("ys-zong/llava-v1.5-7b-Clean-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

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 weight for LLaVA-v1.5-7B re-trained with LoRA after removing harmful samples in the training data. You can use them in exactly the same way as the original LLaVA.

Usage

Please refer to Github for detailed usage.

Downloads last month: 8

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

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-Clean-lora

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

Paper • 2402.02207 • Published Feb 3, 2024 • 2