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tiny-classifiers

Train a 17M-parameter text classifier in a minute, with labels from a big AI model, and know when it beats an API. Recipe, tutorial, results on 10 tasks, phone trainer.

Details

External ID
1390961979
Source
GITHUB
Company
—
Product
tiny-classifiers
Website domain
github.com
Launched
Sept. 27, 2026
Cohort
—
Upvotes
68
Upvotes percentile
0.8829105816038945
Tags
—
Fetched at
Sept. 30, 2026, 5:02 p.m.
Updated at
Sept. 30, 2026, 5:02 p.m.

Enrichment

Theme
AI text humanizers and detectors
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
training framework for small, fast text classification models
Manually corrected
False

Could you build this?

Partial While an interface and basic PyTorch script can be scaffolded, efficient distillation of LLM labels into lightweight classifiers and on-device deployment requires ML engineering know-how.

What it would actually take: Requires a pipeline combining LLM synthetic labeling (OpenAI/Anthropic API), tokenization, and fine-tuning lightweight encoder models (e.g., MobileBERT, ModernBERT, or small Transformers) using PyTorch or ONNX Runtime. Mobile/phone export requires quantization (INT8) and CoreML/TFLite model conversion pipelines.

Competitors

Other products that read as similar to this one — 84 launches clear the similarity bar, closest 8 shown.

Attention rank: #19 of 85 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 326 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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