prompt-intent-mini

A lightweight model for classifying the intent behind a prompt.
Built by TinyModels — small footprint, serious performance.

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What it does

prompt-intent-mini takes a user prompt as input and returns its intent label — what the user is actually trying to do with that prompt. Whether it's a question, a command, a creative request, or something else entirely, the model picks it up fast and with minimal compute.

This is useful anywhere you need to route, filter, or understand prompts before passing them downstream — chatbots, pipelines, safety layers, you name it.

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Quick Start

from transformers import pipeline

classifier = pipeline("text-classification", model="TinyModels/prompt-intent-mini")

result = classifier("Write me a poem about the ocean")
print(result)
# [{'label': 'creative_generation', 'score': 0.97}]

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Model Details

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Why we built this

Most intent classifiers are either too big to run cheaply or too narrow to generalize. We wanted something that sits at the front of a pipeline and just works — fast inference, low memory, no GPU required for most use cases.

prompt-intent-mini follows the same principle as everything we ship at TinyModels: it does one job well, fits anywhere, and doesn't cost you a GPU bill to run.

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Installation

pip install transformers torch

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Intended Use

• Prompt routing in LLM pipelines
• Intent-aware moderation or filtering
• Chatbot understanding layers
• Any application that needs to know what a user wants before acting on it

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Limitations

• Trained on English prompts. Other languages may see reduced accuracy.
• Edge cases with highly ambiguous or mixed-intent prompts may not classify cleanly.
• This is a mini model — for more complex, multi-label scenarios, a larger variant may be more appropriate.

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Part of the TinyModels family

This model was created by TinyModels, a community building small, fast, open models that anyone can run. No paywalls. No gatekeeping.

Tiny models. Huge ambitions.