bakLlava-v1-hf
Maintainer: llava-hf
49
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
bakLlava-v1-hf is a multimodal language model derived from the original LLaVA architecture, using the Mistral-7b text backbone. It is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on a diverse dataset of image-text pairs, GPT-generated multimodal instruction-following data, academic-task-oriented VQA data, and additional private data. According to the maintainer, the model showcases that a Mistral 7B base can outperform Llama 2 13B on several benchmarks. The upcoming BakLLaVA-2 model will feature a significantly larger dataset and a novel architecture that expands beyond the current LLaVA method.
Similar models include the llava-1.5-7b-hf, which uses the original LLaVA 1.5 architecture, and the BakLLaVA-1, which is a Mistral 7B base augmented with the LLaVA 1.5 architecture.
Model inputs and outputs
Inputs
- Image: The model can take one or more images as input, which are then processed by the vision encoder.
- Prompt: The model expects a multi-turn conversation prompt in the format
USER: xxx\nASSISTANT:, with the token<image>inserted where the image should be queried.
Outputs
- Generated text: The model outputs a continuation of the provided prompt, generating relevant responses based on the input image and text.
Capabilities
bakLlava-v1-hf demonstrates strong performance on a variety of multimodal tasks, including image captioning, visual question answering, and open-ended dialogue. The model can understand and reason about the content of images, and provide informative and engaging responses to queries.
What can I use it for?
You can use bakLlava-v1-hf for a wide range of multimodal AI applications, such as:
- Intelligent virtual assistants: Incorporate the model into a chatbot or virtual assistant to enable natural language interactions with images.
- Image-based question answering: Build applications that can answer questions about the content of images.
- Image captioning: Generate descriptive captions for images to support accessibility or improve search and discovery.
Things to try
Experiment with different types of images and prompts to see the model's capabilities in action. Try prompting the model with open-ended questions, task-oriented instructions, or creative scenarios to explore the breadth of its knowledge and language generation abilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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