Phi-3-mini-128k-instruct-onnx
Maintainer: microsoft
159
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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-128k-instruct-onnx is a lightweight, state-of-the-art open model developed by Microsoft. It belongs to the Phi-3 model family, which was trained on synthetic data and filtered websites with a focus on high-quality, reasoning-dense data. 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.
Compared to other similar models, the Phi-3-mini-128k-instruct-onnx is optimized for acceleration with ONNX Runtime, allowing it to run efficiently on a variety of hardware, including CPU, GPU, and mobile devices. This makes it well-suited for memory and compute-constrained environments, as well as latency-bound scenarios. Additionally, the model has demonstrated strong reasoning capabilities, especially in areas like code, math, and logic.
Model inputs and outputs
Inputs
- Text: The
Phi-3-mini-128k-instruct-onnxmodel accepts text as input, and it is best suited for prompts using the chat format.
Outputs
- Generated text: The model generates text in response to the input, with the goal of following instructions and providing safe, ethical, and accurate information.
Capabilities
The Phi-3-mini-128k-instruct-onnx model has been trained to excel at a variety of tasks, including question answering, code generation, and logical reasoning. For example, when prompted to explain the Fermi paradox, the model provides a concise and informative response, highlighting the key ideas behind this intriguing cosmic puzzle.
What can I use it for?
The Phi-3-mini-128k-instruct-onnx model is well-suited for a range of applications that require strong reasoning capabilities, such as research on language and multimodal models, or the development of generative AI features. The model's optimization for ONNX Runtime also makes it a good choice for use cases that require efficient inference on a variety of hardware platforms, including server, desktop, and mobile environments.
Things to try
One interesting thing to try with the Phi-3-mini-128k-instruct-onnx model is to explore its ability to generate code snippets. While the model has been trained on a range of data sources, including common programming languages and libraries, it's important to carefully validate any generated code before using it in production, as the model may produce inaccurate or unsafe output. Additionally, you could experiment with prompting the model to perform more complex logical reasoning tasks, such as solving mathematical problems or analyzing ethical dilemmas, to see how it responds.
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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