Phi-3-mini-4k-instruct
Maintainer: microsoft
603
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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 is a 3.8B parameter, lightweight, state-of-the-art open model trained with the Phi-3 datasets, as described by the maintainer. It is part of the Phi-3 family of models, which includes other variants like the phi-3-mini-128k-instruct and phi-3-mini-128k-instruct that differ in their context length. The Phi-3 models are designed to be high-performing yet memory/compute-constrained, making them suitable for latency-bound scenarios and environments with limited resources.
Model inputs and outputs
The phi-3-mini-4k-instruct model takes text as input and generates text as output. It is particularly well-suited for prompts using a chat format, where the input is structured as a conversation between a user and an assistant.
Inputs
- Prompt: The text that the model will use to generate a response.
- System Prompt: An optional system prompt that helps guide the model's behavior, such as instructing it to act as a helpful assistant.
- Additional parameters: The model also accepts various parameters to control the generation process, such as temperature, top-k and top-p filtering, and stopping sequences.
Outputs
- Generated Text: The model's response to the provided prompt, which can be a continuation of the conversation, an answer to a question, or a generated piece of text.
Capabilities
The phi-3-mini-4k-instruct model has been fine-tuned to excel at tasks that require strong reasoning abilities, such as common sense reasoning, language understanding, math, coding, and logical reasoning. When evaluated on a range of benchmarks, the model has demonstrated state-of-the-art performance among models with less than 13 billion parameters.
What can I use it for?
The phi-3-mini-4k-instruct model is intended for a variety of commercial and research use cases in English, particularly those that require memory or compute-constrained environments, such as mobile applications, or latency-bound scenarios. It can be used as a building block for developing generative AI-powered features, such as chatbots, question-answering systems, and code generation tools.
Things to try
One interesting aspect of the phi-3-mini-4k-instruct model is its ability to engage in multi-turn conversations using the provided chat format. You can try prompting the model with a series of related questions or tasks and observe how it maintains context and generates coherent responses. Additionally, the model's strong performance on tasks like math and coding make it a compelling choice for developing educational or productivity-focused 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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