Jartine

llava-v1.5-7B-GGUF

jartine

153

The llava-v1.5-7B-GGUF model is an open-source chatbot trained by fine-tuning the LLaMA/Vicuna language model on a diverse dataset of GPT-generated multimodal instruction-following data. It is an auto-regressive language model based on the transformer architecture, developed by the researcher jartine. The model was trained in September 2023 and is licensed under the LLAMA 2 Community License. Similar models include the LLaVA-13b-delta-v0, llava-v1.6-mistral-7b, llava-1.5-7b-hf, and ShareGPT4V-7B, all of which are multimodal chatbot models based on the LLaVA architecture. Model inputs and outputs Inputs Image:** The model can process and generate responses based on provided images. Text prompt:** The model takes in a text-based prompt, typically following a specific template, to generate a response. Outputs Text response:** The model generates a text-based response based on the provided image and prompt. Capabilities The llava-v1.5-7B-GGUF model is capable of performing a variety of multimodal tasks, such as image captioning, visual question answering, and instruction-following. It can generate coherent and relevant responses to prompts that involve both text and images, drawing on its training on a diverse dataset of multimodal instruction-following data. What can I use it for? The primary use of the llava-v1.5-7B-GGUF model is for research on large multimodal models and chatbots. It can be utilized by researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence to explore the capabilities and limitations of such models. Additionally, the model's ability to process and respond to multimodal prompts could be leveraged in various applications, such as chatbots, virtual assistants, and educational tools. Things to try One interesting aspect of the llava-v1.5-7B-GGUF model is its potential to combine visual and textual information in novel ways. Experimenters could try providing the model with prompts that involve both images and text, and observe how it synthesizes the information to generate relevant and coherent responses. Additionally, users could explore the model's capabilities in handling complex or ambiguous prompts, or prompts that require reasoning about the content of the image.

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