Gryphe/Gemma-4-26B-A4B-StyleTune-V2
Text Generation • 27B • Updated • 110 • 22
Denis Rodionov
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#8 opened 9 days ago
by 
Hypersniper
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I built Read-Along AI for the Hugging Face Build Small Hackathon.
It is an offline-capable reading practice app for early readers: one short sentence at a time, tap-to-hear word help, record a read-aloud attempt, then get gentle feedback.
The goal is Backyard AI in the literal sense: a tool for real home reading practice, where feedback needs to be patient, developmentally fair, and private. A child’s voice should not need to leave the app just to practice “The dog ran fast.”
What makes it small-model native:
- Exact clean readings pass immediately.
- Close or ambiguous child-speech transcripts get a second look from a fine-tuned MiniCPM phonetic evaluator.
- Meaning-changing mistakes still fail closed, e.g. “blue hat” should not pass for “red hat.”
- Off the Grid Mode runs local ASR plus the MiniCPM GGUF evaluator through llama.cpp.
- Turbo Mode uses Modal endpoints for lower-latency ASR/TTS/evaluation.
- The UI is custom Gradio with a child-facing reading canvas, clickable words, progress feedback, and celebration on success.
Targeted tracks and badges:
Backyard AI, Off-Brand, Off the Grid, Llama Champion, Well-Tuned, Tiny Titan, Sharing is Caring, Field Notes.
Space:
build-small-hackathon/read-along-ai
Demo video:
https://youtu.be/4bpbwhipLU4
Repo:
https://github.com/kingkw1/read-along-ai
Built with Codex as the lead development partner.
It is an offline-capable reading practice app for early readers: one short sentence at a time, tap-to-hear word help, record a read-aloud attempt, then get gentle feedback.
The goal is Backyard AI in the literal sense: a tool for real home reading practice, where feedback needs to be patient, developmentally fair, and private. A child’s voice should not need to leave the app just to practice “The dog ran fast.”
What makes it small-model native:
- Exact clean readings pass immediately.
- Close or ambiguous child-speech transcripts get a second look from a fine-tuned MiniCPM phonetic evaluator.
- Meaning-changing mistakes still fail closed, e.g. “blue hat” should not pass for “red hat.”
- Off the Grid Mode runs local ASR plus the MiniCPM GGUF evaluator through llama.cpp.
- Turbo Mode uses Modal endpoints for lower-latency ASR/TTS/evaluation.
- The UI is custom Gradio with a child-facing reading canvas, clickable words, progress feedback, and celebration on success.
Targeted tracks and badges:
Backyard AI, Off-Brand, Off the Grid, Llama Champion, Well-Tuned, Tiny Titan, Sharing is Caring, Field Notes.
Space:
build-small-hackathon/read-along-ai
Demo video:
https://youtu.be/4bpbwhipLU4
Repo:
https://github.com/kingkw1/read-along-ai
Built with Codex as the lead development partner.
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#1 opened 10 days ago
by 
AliceThirty
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