Prompt Engineering on a Small On-Device Model Is a Different Sport
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I built Redacto with my team in a single day at a hackathon: an on-device PII redaction app that runs a quantized small model entirely on a phone. No cloud calls, no API keys, no data leaving the device. The model is Gemma 4 E2B Instruct (roughly 2.3B effective parameters, 5.1B total, using Per-Layer Embeddings, where the E stands for effective), quantized to INT4 and running inside LiteRT-LM on a Snapdragon 8 Elite. Before this project, I thought I understood prompt engineering. I had written…
1Key Takeaways
- I built Redacto with my team in a single day at a hackathon: an on-device PII redaction app that runs a quantized small model entirely on a phone.
- No cloud calls, no API keys, no data leaving the device.
- The model is Gemma 4 E2B Instruct (roughly 2.3B effective parameters, 5.1B total, using Per-Layer Embeddings, where the E stands for effective), quantized to INT4 and running inside LiteRT-LM on a Snapdragon 8 Elite.
- Before this project, I thought I understood prompt engineering.
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3Why it matters
Prompt and agent patterns spread fast; staying current saves time and token cost. DEV — Prompt Engineering reports that i built Redacto with my team in a single day at a hackathon: an on-device PII redaction app that runs a quantized small model entirely on a phone.
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