Instructions to use lmms-lab/LLaVA-NeXT-Video-7B-32K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmms-lab/LLaVA-NeXT-Video-7B-32K with Transformers:
# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("lmms-lab/LLaVA-NeXT-Video-7B-32K", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| inference: false | |
| license: apache-2.0 | |
| pipeline_tag: video-text-to-text | |
| <br> | |
| # LLaVA-Next-Video Model Card | |
| ## Model details | |
| **Model type:** | |
| <br> | |
| LLaVA-Next-Video is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. | |
| <br> | |
| Base LLM: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | |
| **Model date:** | |
| <br> | |
| LLaVA-Next-Video-7B-34K was trained in April 2024. | |
| **Paper or resources for more information:** | |
| <br> | |
| https://github.com/LLaVA-VL/LLaVA-NeXT | |
| ## License | |
| Llama 2 is licensed under the LLAMA 2 Community License, | |
| Copyright (c) Meta Platforms, Inc. All Rights Reserved. | |
| ## Where to send questions or comments about the model | |
| https://github.com/LLaVA-VL/LLaVA-NeXT/issues | |
| ## Intended use | |
| **Primary intended uses:** | |
| <br> | |
| The primary use of LLaVA is research on large multimodal models and chatbots. | |
| **Primary intended users:** | |
| <br> | |
| The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. | |
| ## Training dataset | |
| ### Image | |
| - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. | |
| - 158K GPT-generated multimodal instruction-following data. | |
| - 500K academic-task-oriented VQA data mixture. | |
| - 50K GPT-4V data mixture. | |
| - 40K ShareGPT data. | |
| ### Video | |
| - 100K VideoChatGPT-Instruct. | |
| ## Evaluation dataset | |
| A collection of 4 benchmarks, including 3 academic VQA benchmarks and 1 captioning benchmark. |