llava-v1.6-34b
Maintainer: liuhaotian - Last updated 5/27/2024
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Model overview
The llava-v1.6-34b is an open-source chatbot developed by liuhaotian that is trained by fine-tuning a large language model (LLM) on multimodal instruction-following data. It is based on the transformer architecture and uses the NousResearch/Nous-Hermes-2-Yi-34B as its base LLM.
The model is part of the LLaVA family, which includes similar versions like llava-v1.5-13b, llava-v1.5-7b, llava-v1.6-mistral-7b, and LLaVA-13b-delta-v0. These models differ in their base LLM, training dataset, and model size.
Model inputs and outputs
Inputs
- The model accepts natural language instructions and prompts as input.
- It can also accept image data as input for multimodal tasks.
Outputs
- The model generates human-like responses in natural language.
- For multimodal tasks, the model can generate relevant images as output.
Capabilities
The llava-v1.6-34b model has been trained to engage in a wide range of tasks, including natural language processing, computer vision, and multimodal reasoning. It has shown strong performance on tasks such as answering complex questions, following detailed instructions, and generating relevant images.
What can I use it for?
The primary use of the llava-v1.6-34b model is for research on large multimodal models and chatbots. It can be particularly useful for researchers and hobbyists working in computer vision, natural language processing, machine learning, and artificial intelligence.
Some potential use cases for the model include:
- Building chatbots and virtual assistants with multimodal capabilities
- Developing visual question answering systems
- Exploring new techniques for instruction-following in language models
- Advancing research on multimodal reasoning and understanding
Things to try
One interesting aspect of the llava-v1.6-34b model is its ability to combine text and image data to perform complex tasks. Researchers could experiment with using the model to generate images based on textual descriptions, or to answer questions that require both visual and linguistic understanding.
Another area to explore is the model's performance on tasks that require strong reasoning and problem-solving skills, such as scientific question answering or task-oriented dialogue. By probing the model's capabilities in these areas, researchers can gain valuable insights into the strengths and limitations of large multimodal language models.
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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