Phi-3-mini-128k-instruct
Maintainer: microsoft
1.3K
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| Property | Value |
|---|---|
| Model Link | View 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 is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K, which is the context length (in tokens) that it can support.
After initial training, the model underwent a post-training process that involved supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures. When evaluated against benchmarks that test common sense, language understanding, mathematics, coding, long-term context, and logical reasoning, the Phi-3 Mini-128K-Instruct demonstrated robust and state-of-the-art performance among models with fewer than 13 billion parameters.
Model inputs and outputs
Inputs
- Text prompts
Outputs
- Generated text responses
Capabilities
The Phi-3-mini-128k-instruct model is designed to excel in memory/compute constrained environments, latency-bound scenarios, and tasks requiring strong reasoning skills, especially in areas like code, math, and logic. It can be used to accelerate research on language and multimodal models, serving as a building block for generative AI-powered features.
What can I use it for?
The Phi-3-mini-128k-instruct model is intended for commercial and research use in English. It can be particularly useful for applications that require efficient performance in resource-constrained settings or low-latency scenarios, such as mobile devices or edge computing environments. Given its strong reasoning capabilities, the model can be leveraged for tasks involving coding, mathematical reasoning, and logical problem-solving.
Things to try
One interesting aspect of the Phi-3-mini-128k-instruct model is its ability to perform well on benchmarks testing common sense, language understanding, and logical reasoning, even with a relatively small parameter count compared to larger language models. This suggests it could be a useful starting point for exploring ways to build efficient and capable AI assistants that can understand and reason about the world in a robust manner.
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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