phi-3-mini-128k-instruct
Maintainer: lucataco
4
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| 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. It is part of the Phi-3 family of models, which also includes the Phi-3-mini-4k-instruct variant. Both models have undergone a post-training process that incorporates supervised fine-tuning and direct preference optimization to enhance their ability to follow instructions and adhere to safety measures.
Model inputs and outputs
The phi-3-mini-128k-instruct model is best suited for text-based inputs, particularly prompts using a chat format. It can generate relevant and coherent responses to a wide range of queries, drawing upon its extensive training on high-quality data.
Inputs
- Prompt: The text prompt to be processed by the model.
- System Prompt: An optional system prompt that sets the tone and context for the assistant.
- Additional parameters: The model also accepts various parameters to control the output, such as temperature, top-k, top-p, and repetition penalty.
Outputs
- Generated text: The model's response to the input prompt, generated in an iterative manner.
Capabilities
The phi-3-mini-128k-instruct model has demonstrated strong performance on a variety of benchmarks testing common sense, language understanding, mathematics, coding, long-term context, and logical reasoning. It is particularly adept at tasks that require robust reasoning and understanding, such as solving complex math problems or generating code snippets.
What can I use it for?
The phi-3-mini-128k-instruct model is intended for commercial and research use in English-language applications. It is well-suited for memory and compute-constrained environments, as well as latency-bound scenarios that require strong reasoning capabilities. Potential use cases include:
- Developing AI-powered features for applications that leverage language understanding and generation
- Accelerating research on language and multimodal models
- Deploying in environments with limited resources, such as edge devices or mobile applications
Things to try
One interesting aspect of the phi-3-mini-128k-instruct model is its ability to engage in coherent, context-aware dialogue. Try providing the model with a series of related prompts or questions, and observe how it maintains and builds upon the conversation. You can also experiment with different parameter settings, such as adjusting the temperature or top-k/top-p values, to see how they affect the model's output.
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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