Instructions to use Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5 with Transformers:

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

pipe = pipeline("text-generation", model="Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5

SGLang

How to use Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5 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 "Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5" \
    --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": "Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5",
		"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 "Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5" \
        --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": "Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5 with Docker Model Runner:
```
docker model run hf.co/Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5
```

ShareGPT4V-7B_Pretrained_vit-large336-l12_vicuna-7b-v1.5 / README.md

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	---
	inference: false
	---
	<br>
	<br>

	# ShareGPT4V-7B Model Card

	## Model details

	Model type:
	This is the pre-trained LLM and projector of ShareGPT4v-7B

	Model date:
	ShareGPT4V-7B-Pretrained was trained in Nov 2023.

	Paper or resources for more information:
	[[Project](https://ShareGPT4V.github.io/)] [[Paper](https://huggingface.co/papers/2311.12793)] [[Code](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)] [[Dataset](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V)]

	## Usage
	You can directly utilize this model as we provide in our [[repository](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)]. Moreover, you can modify the architecture name from "Share4VLlamaForCausalLM" to "LLaVALlamaForCausalLM" and the model_type keyword from "share4v" to "llava" in our config file and seamlessly load our model in the [[LLaVA repository](https://github.com/haotian-liu/LLaVA)].

	## License
	Llama 2 is licensed under the LLAMA 2 Community License,
	Copyright (c) Meta Platforms, Inc. All Rights Reserved.

	## Intended use
	Primary intended uses:
	The primary use of ShareGPT4V-7B 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
	- 1.2M high-quality image-text pairs, i.e., ShareGPT4V-PT data