Instructions to use yifanzhang114/SliME-Llama3-8B-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yifanzhang114/SliME-Llama3-8B-lora with Transformers:

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

pipe = pipeline("image-text-to-text", model="yifanzhang114/SliME-Llama3-8B-lora")

# Load model directly
from transformers import AutoModelForMultimodalLM
model = AutoModelForMultimodalLM.from_pretrained("yifanzhang114/SliME-Llama3-8B-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yifanzhang114/SliME-Llama3-8B-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yifanzhang114/SliME-Llama3-8B-lora"
# 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-Llama3-8B-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yifanzhang114/SliME-Llama3-8B-lora

SGLang

How to use yifanzhang114/SliME-Llama3-8B-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 "yifanzhang114/SliME-Llama3-8B-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": "yifanzhang114/SliME-Llama3-8B-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 "yifanzhang114/SliME-Llama3-8B-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": "yifanzhang114/SliME-Llama3-8B-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yifanzhang114/SliME-Llama3-8B-lora with Docker Model Runner:
```
docker model run hf.co/yifanzhang114/SliME-Llama3-8B-lora
```

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: meta-llama/Meta-Llama-3-8B-Instruct

Paper or resources for more information: 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.

Training dataset

SharedGPT4v sft data
SMR data

Evaluation dataset

A collection of 15 benchmarks, including 5 academic VQA benchmarks and 10 recent benchmarks specifically proposed for instruction-following LMMs.

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SliME-Llama3-8B-lora

SliME Model Card

Model details

License

Intended use

Training dataset

Evaluation dataset

Dataset used to train yifanzhang114/SliME-Llama3-8B-lora

Collection including yifanzhang114/SliME-Llama3-8B-lora

SliME