Instructions to use lmms-lab/llava-next-interleave-qwen-7b-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmms-lab/llava-next-interleave-qwen-7b-dpo with Transformers:

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

pipe = pipeline("text-generation", model="lmms-lab/llava-next-interleave-qwen-7b-dpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("lmms-lab/llava-next-interleave-qwen-7b-dpo", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lmms-lab/llava-next-interleave-qwen-7b-dpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmms-lab/llava-next-interleave-qwen-7b-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmms-lab/llava-next-interleave-qwen-7b-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lmms-lab/llava-next-interleave-qwen-7b-dpo

SGLang

How to use lmms-lab/llava-next-interleave-qwen-7b-dpo 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 "lmms-lab/llava-next-interleave-qwen-7b-dpo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmms-lab/llava-next-interleave-qwen-7b-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "lmms-lab/llava-next-interleave-qwen-7b-dpo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmms-lab/llava-next-interleave-qwen-7b-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lmms-lab/llava-next-interleave-qwen-7b-dpo with Docker Model Runner:
```
docker model run hf.co/lmms-lab/llava-next-interleave-qwen-7b-dpo
```

LLaVA-Next Interleave Model Card

Model Details

Model type: LLaVA-Next Interleave 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: Qwen/Qwen1.5-7B-Chat

Model Description

Repository: https://github.com/LLaVA-VL/LLaVA-NeXT

Primary intended uses: The primary use of LLaVA-Next Interleave is research on large multimodal models and chatbots. This is only for research exploration, and prohibited for commercial usage.

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.

License Notices

This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the OpenAI Terms of Use for the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g. Llama-1/2 community license for LLaMA-2 and Vicuna-v1.5, Tongyi Qianwen LICENSE AGREEMENT and META LLAMA 3 COMMUNITY LICENSE AGREEMENT). This project does not impose any additional constraints beyond those stipulated in the original licenses. Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations.

How to Get Started with the Model

Use the code below to get started with the model.

git clone https://github.com/LLaVA-VL/LLaVA-NeXT
# install llava-next
...
# download the ckpt
...
bash playground/demo/interleave_demo.py --model_path path/to/ckpt

Evaluation

Use the code below to evaluate the model.

Please first edit /path/to/ckpt to the path of checkpoint, /path/to/images to the path of "interleave_data" in scripts/interleave/eval_all.sh and then run

bash scripts/interleave/eval_all.sh

Bibtex citation

@misc{li2024llavanextinterleavetacklingmultiimagevideo,
      title={LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models}, 
      author={Feng Li and Renrui Zhang and Hao Zhang and Yuanhan Zhang and Bo Li and Wei Li and Zejun Ma and Chunyuan Li},
      year={2024},
      eprint={2407.07895},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.07895}, 
}

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Safetensors

Model size

8B params

Tensor type

BF16

Collection including lmms-lab/llava-next-interleave-qwen-7b-dpo

LLaVA-Next-Interleave

Collection

7 items • Updated Oct 4, 2024 • 16

Paper for lmms-lab/llava-next-interleave-qwen-7b-dpo

LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Paper • 2407.07895 • Published Jul 10, 2024 • 42