Instructions to use yifanzhang114/SliME-vicuna-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yifanzhang114/SliME-vicuna-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="yifanzhang114/SliME-vicuna-7B")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("yifanzhang114/SliME-vicuna-7B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yifanzhang114/SliME-vicuna-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yifanzhang114/SliME-vicuna-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yifanzhang114/SliME-vicuna-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yifanzhang114/SliME-vicuna-7B

SGLang

How to use yifanzhang114/SliME-vicuna-7B 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 "yifanzhang114/SliME-vicuna-7B" \
    --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": "yifanzhang114/SliME-vicuna-7B",
		"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 "yifanzhang114/SliME-vicuna-7B" \
        --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": "yifanzhang114/SliME-vicuna-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yifanzhang114/SliME-vicuna-7B with Docker Model Runner:
```
docker model run hf.co/yifanzhang114/SliME-vicuna-7B
```

SliME Model Card

Model details

Model type: SliME is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: lmsys/vicuna-7b-v1.5

Paper or resources for more information:

Paper: https://huggingface.co/papers/2406.08487

Arxiv: https://arxiv.org/abs/2406.08487

Code: https://github.com/yfzhang114/SliME

License

Where to send questions or comments about the model: https://github.com/yfzhang114/SliME/issues

Intended use

Primary intended uses: The primary use of SliME 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.