Mistral-7B-Instruct-v0.3-GGUF
Maintainer: MaziyarPanahi
54
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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 Mistral-7B-Instruct-v0.3-GGUF model is a text-to-text AI model created by Mistral AI. It is an instruction-tuned version of the Mistral-7B-v0.1 model, which outperforms the Llama 2 13B model on various benchmarks. The model uses grouped-query attention, sliding-window attention, and a byte-fallback BPE tokenizer in its architecture.
Similar models include the Llama-3-8B-Instruct-32k-v0.1-GGUF and the Mistral-7B-Instruct-v0.1-GGUF, both of which are also instruction-tuned large language models.
Model inputs and outputs
Inputs
- Text prompts that can be used to instruct the model to perform various tasks, such as answering questions, generating text, or completing tasks.
Outputs
- Generated text outputs that respond to the provided prompts, following the given instructions.
Capabilities
The Mistral-7B-Instruct-v0.3-GGUF model is capable of generating coherent and contextually appropriate text in response to a wide range of prompts. It can be used for tasks such as question answering, text summarization, creative writing, and task completion. The model has been fine-tuned on instructional datasets to improve its ability to follow instructions and complete tasks.
What can I use it for?
The Mistral-7B-Instruct-v0.3-GGUF model can be used for a variety of applications, such as developing virtual assistants, chatbots, or content generation tools. It could be used by companies to automate customer service tasks, generate marketing copy, or create personalized content for their customers. Researchers and developers could also use the model as a starting point for fine-tuning or further development of language models for their specific needs.
Things to try
One interesting thing to try with the Mistral-7B-Instruct-v0.3-GGUF model is to provide it with multi-step instructions or tasks, and see how well it is able to follow and complete them. You could also experiment with different prompting techniques, such as using targeted questions or providing additional context, to see how the model's outputs change.
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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