Phi-3-mini-4k-instruct-onnx
Maintainer: microsoft
83
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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
Phi-3-mini-4k-instruct-onnx is a lightweight, state-of-the-art AI model developed by Microsoft that is optimized for inference with ONNX Runtime. It is part of the Phi-3 model family, which includes both 4K and 128K variants. The model was trained on a combination of synthetic data, filtered websites, and high-quality chat format data, undergoing a rigorous enhancement process to ensure precise instruction adherence and robust safety measures.
The optimized Phi-3-mini-4k-instruct-onnx model is published in ONNX format to enable accelerated inference on a variety of hardware, including CPU, GPU, and mobile devices. It supports DirectML for hardware acceleration on Windows devices, and can run on different NVIDIA GPU architectures using CUDA. There are also INT4-quantized versions available for improved performance on CPUs and mobile devices.
Similar models in the Phi-3 family include the Phi-3-mini-128k-instruct-onnx and the Phi-3-mini-4k-instruct models, which offer different context length support.
Model Inputs and Outputs
Inputs
- Text: The
Phi-3-mini-4k-instruct-onnxmodel is best suited for prompts using a chat format, where the input is formatted as a question or instruction.
Outputs
- Generated Text: The model generates text in response to the input prompt, following the instruction or answering the question.
Capabilities
The Phi-3-mini-4k-instruct-onnx model has been trained to demonstrate strong reasoning abilities, including common sense reasoning, logical reasoning, and following instructions precisely. It has been evaluated on a variety of benchmarks, such as MMLU, HellaSwag, and TruthfulQA, where it has shown state-of-the-art performance compared to other models of similar size.
What Can I Use It For?
The Phi-3-mini-4k-instruct-onnx model is well-suited for use cases that require a lightweight, high-performance model with robust reasoning capabilities. Some potential applications include:
- Memory/compute-constrained environments: The model's small size and optimized ONNX format make it suitable for deployment on devices with limited resources, such as mobile phones or edge devices.
- Latency-bound scenarios: The model's optimized inference performance can be beneficial in applications that require fast responses, such as chatbots or virtual assistants.
- Applications requiring strong reasoning: The model's strong performance on benchmarks testing common sense, math, coding, and logical reasoning makes it a good choice for applications that require these capabilities, such as educational tools or coding assistants.
Microsoft has also provided ONNX Runtime integration and support, making it easier to deploy the Phi-3-mini-4k-instruct-onnx model across a range of platforms and hardware.
Things to Try
One interesting aspect of the Phi-3-mini-4k-instruct-onnx model is its support for different precision levels, including INT4 quantization for improved performance on CPUs and mobile devices. You could try experimenting with these different model configurations to see how they perform on your specific use case and hardware.
Additionally, the model's strong reasoning capabilities could be useful for building educational or productivity-focused applications, where users can interact with the model to get assistance with tasks like math, coding, or general knowledge questions. You could explore ways to leverage the model's strengths in these areas.
Finally, the availability of the model in ONNX format and the provided ONNX Runtime integration opens up opportunities for cross-platform deployment and hardware acceleration. You could investigate how to take advantage of these features to optimize the model's performance and deployment for your target platforms and devices.
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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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-onnx model 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.
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