llava-1.6-mistral-7b-gguf
Maintainer: cjpais
65
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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
The llava-1.6-mistral-7b-gguf is an open-source chatbot model developed by cjpais that is based on the mistralai/Mistral-7B-Instruct-v0.2 language model. It was fine-tuned on multimodal instruction-following data to improve its conversational and task-completion abilities. The model is available in several quantized versions ranging from 2-bit to 8-bit precision, providing trade-offs between file size, CPU/GPU memory usage, and inference quality.
Model inputs and outputs
Inputs
- Text prompts: The model takes free-form text prompts as input, which can include instructions, questions, or other types of conversational input.
Outputs
- Generated text: The model outputs generated text, which can include responses, completions, or other forms of generated content.
Capabilities
The llava-1.6-mistral-7b-gguf model is capable of engaging in a wide range of conversational tasks, such as answering questions, providing explanations, and following instructions. It can also be used for content generation, summarization, and other natural language processing applications.
What can I use it for?
The llava-1.6-mistral-7b-gguf model can be used for a variety of research and commercial applications, such as building chatbots, virtual assistants, and other conversational AI systems. Its multimodal instruction-following capabilities make it well-suited for tasks that require understanding and executing complex instructions, such as creative writing, task planning, and data analysis.
Things to try
One interesting thing to try with the llava-1.6-mistral-7b-gguf model is to experiment with different prompting strategies and instruction formats. The model's instruction-following abilities can be leveraged to create more engaging and interactive conversational experiences. Additionally, you can try combining the model with other AI systems or data sources to develop more sophisticated applications.
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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