Phi-3-mini-4k-instruct
Maintainer: unsloth
41
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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 Phi-3-mini-4k-instruct model is a lightweight, state-of-the-art open model developed by unsloth that builds upon datasets used for Phi-2, with a focus on high-quality, reasoning-dense data. The model is part of the Phi-3 family and comes in two variants: 4K and 128K, which refers to the maximum context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization, to ensure precise instruction adherence and robust safety measures.
The Phi-3-mini-4k-instruct model is similar to other models in the Phi-3 family, such as the llama-3-8b-instruct, llama-3-8b-bnb-4bit, and Phi-3-mini-4k-instruct-onnx models, all of which are optimized for improved performance and efficiency.
Model inputs and outputs
Inputs
- Text prompt: The model takes in a text prompt, which can be a natural language query, instruction, or any other text input.
Outputs
- Text response: The model generates a relevant text response based on the input prompt.
Capabilities
The Phi-3-mini-4k-instruct model is a powerful natural language processing model that can be used for a variety of tasks, such as text generation, question answering, and language understanding. It is particularly well-suited for tasks that require precise instruction adherence and reasoning, as it has been optimized for these capabilities.
What can I use it for?
The Phi-3-mini-4k-instruct model can be used for a wide range of applications, such as chatbots, virtual assistants, language translation, and content generation. Its compact size and efficient performance make it a great choice for deployment on a variety of platforms, from mobile devices to cloud-based services.
Things to try
One interesting aspect of the Phi-3-mini-4k-instruct model is its ability to generate high-quality, coherent text while using significantly less memory and processing power than larger language models. You could try fine-tuning the model on your own dataset to see how it performs on specific tasks, or experiment with different prompting techniques to unlock its full potential.
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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