Instructions to use SnurfyAI/is-even with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SnurfyAI/is-even with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SnurfyAI/is-even")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SnurfyAI/is-even") model = AutoModelForSequenceClassification.from_pretrained("SnurfyAI/is-even") - Notebooks
- Google Colab
- Kaggle
Peter Schnurf commited on
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`Is Even` is a tiny decoder-only transformer trained on chat-formatted synthetic data from SnurfyAI that predicts whether a decimal input is even.
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This repository contains the published model weights and
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## What The Model Does
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The expected completion label is `true` or `false`.
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- The current release focuses on weights and metadata only.
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- Public training/inference utilities are not yet released.
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`Is Even` is a tiny decoder-only transformer trained on chat-formatted synthetic data from SnurfyAI that predicts whether a decimal input is even.
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This repository contains the published model weights and metadata for the released checkpoint.
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## What The Model Does
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The expected completion label is `true` or `false`.
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## Training Code
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You can train the model yourself using the official SnurfyAI training codebase:
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- [SnurfyAI Is Even Core](https://github.com/SnurfyAI/is-even)
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The repository contains the code needed to generate the dataset, train the model, and benchmark saved checkpoints.
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## Research Paper
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- [A Microscopic Study of Decimal Parity in a Tiny Causal Transformer](https://ai.snurfy.net/research/is-even)
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The accompanying paper analyzes the model’s generalization behavior, attention structure, and causal dependence on the final digit.
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