Instructions to use bfshi/llava-v1.5-7b-s2-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bfshi/llava-v1.5-7b-s2-lora with Transformers:

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

pipe = pipeline("text-generation", model="bfshi/llava-v1.5-7b-s2-lora")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("bfshi/llava-v1.5-7b-s2-lora")
model = AutoModelForCausalLM.from_pretrained("bfshi/llava-v1.5-7b-s2-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bfshi/llava-v1.5-7b-s2-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bfshi/llava-v1.5-7b-s2-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bfshi/llava-v1.5-7b-s2-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bfshi/llava-v1.5-7b-s2-lora

SGLang

How to use bfshi/llava-v1.5-7b-s2-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 "bfshi/llava-v1.5-7b-s2-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": "bfshi/llava-v1.5-7b-s2-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 "bfshi/llava-v1.5-7b-s2-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": "bfshi/llava-v1.5-7b-s2-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bfshi/llava-v1.5-7b-s2-lora with Docker Model Runner:
```
docker model run hf.co/bfshi/llava-v1.5-7b-s2-lora
```

When Do We Not Need Larger Vision Models?

Model

This is a LLaVA-v1.5-7b model trained with S²-Wrapper, a simple approach to enable any vision model to perceive high-resolution images. We use image resolutions of up to 1008x1008 for this model.

Training

The training pipeline and dataset completely follow LLaVA-v1.5. We use LoRA to fine-tune the model.

Benchmarking

Version	Size	Schedule	Checkpoint	VQAv2	VizWiz	TextVQA	MMMU-val	MathVista	MM-Bench	SEED	MM-Vet
LLaVA-1.5	7B	full_ft-1e	liuhaotian/llava-v1.5-7b	78.5	50.0	58.2	36.2	25.2	64.3	65.7	31.1
LLaVA-1.5	7B	lora-1e	liuhaotian/llava-v1.5-7b-lora	79.1	47.8	58.2	-	-	66.1	-	30.2
LLaVA-1.5-S2	7B	lora-1e	this model	80.0	50.1	61.0	37.7	25.3	66.2	67.9	32.4

License

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