Maintainer: nisten

Total Score


Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The obsidian-3b-multimodal-q6-gguf model is a large language model created by the AI researcher nisten. It is a quantized version of the original Nous Research Obsidian-3B-V0.5 model, optimized for efficient text-to-image generation on CPUs and GPUs.

Similar models include the saiga_mistral_7b_gguf from IlyaGusev, which is a Llama.cpp compatible version of a 7B language model, and the gpt4-x-vicuna-13B-GGML from TheBloke, which is a GGML format version of a GPT-4 derived 13B language model.

Model inputs and outputs


  • Text prompts: The model takes in text prompts that can be used to generate images.


  • Images: The model outputs generated images based on the text prompts provided as input.


The obsidian-3b-multimodal-q6-gguf model is capable of generating high-quality images from text prompts. It can be used for a variety of text-to-image tasks, such as creating illustrations, generating product mockups, and visualizing abstract concepts. The model's quantization allows for efficient inference on both CPUs and GPUs, making it suitable for a range of deployment scenarios.

What can I use it for?

The obsidian-3b-multimodal-q6-gguf model can be used in a variety of applications that require generating images from text. For example, it could be used in content creation tools to automatically generate images to accompany blog posts or social media updates. It could also be used in e-commerce applications to generate product mockups or visualizations based on customer descriptions.

Things to try

One interesting thing to try with the obsidian-3b-multimodal-q6-gguf model is to experiment with different prompt styles and structures to see how they affect the generated images. For example, you could try providing more detailed or specific prompts, or prompts that incorporate creative or descriptive language. Additionally, you could try combining the model with other tools or libraries, such as image editing software or natural language processing frameworks, to create more complex or customized image generation workflows.

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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