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Independent publisher

Aayush Pokharel

Aayush9029

Swiftie

Models in Library2
Datasets in Library0
Models on Hugging Face7
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This profile connects the 2 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.

Published tasks: Text generation.

Reported licenses: apache-2.0. Open a record to review its specific license and conditions.

Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.

Models

Model · Text generation

petal-w1-4bit

Aayush Pokharel

petal-w1 turns raw dictation into the text you meant to type. It is the on-device cleanup model in Petal, a free macOS dictation app. It runs with MLX on Apple silicon. It is a full fine-tune of Qwen3.5-0.8B, and it does one job: - Removes fillers, stutters, false starts, and verbal tics ("you know what I mean?"). - Keeps only the final version of a self-correction ("Friday, no wait, Thursday" becomes "Thursday"), also when speech recognition mishears the correction words. - States a point once when the speaker repeats it in different words, and keeps every detail. - Condenses rambles into clear sentences, and keeps every fact, name, number, and request. - Keeps questions as questions and…

Open weights apache-2.0 752M parameters 262,144 tokens mlx

Model · Text generation

petal-w1

Aayush Pokharel

petal-w1 turns raw dictation into the text you meant to type. It is the on-device cleanup model in Petal, a free macOS dictation app. It runs with MLX on Apple silicon. It is a full fine-tune of Qwen3.5-0.8B, and it does one job: - Removes fillers, stutters, false starts, and verbal tics ("you know what I mean?"). - Keeps only the final version of a self-correction ("Friday, no wait, Thursday" becomes "Thursday"), also when speech recognition mishears the correction words. - States a point once when the speaker repeats it in different words, and keeps every detail. - Condenses rambles into clear sentences, and keeps every fact, name, number, and request. - Keeps questions as questions and…

Open weights apache-2.0 752M parameters 262,144 tokens mlx