Downloads · 30 days
6
18% of all-time downloads
yashwork-byte/gemma-3-4b-it-voice-digest-lora
gemma-3-4b-it-voice-digest-lora is a translation model from yashwork-byte. Use it when you need text moved from one language to another. It is set up for peft. The card lists the license as gemma.
LoRA adapter that turns Gemma-3-4B-it into a WhatsApp-voice-note digester for Indic languages: given a raw STT transcript (Hindi, Hinglish, Tamil, Bengali), it emits schema-exact JSON with an English summary, translat…
Downloads · 30 days
6
18% of all-time downloads
All-time downloads
34
Public
Repo size
153 MB
Likes
0
Public
Click a slice to open those files.
.safetensors119 MB · 78%
From the Hugging Face model README
LoRA adapter that turns Gemma-3-4B-it into a WhatsApp-voice-note digester
for Indic languages: given a raw STT transcript (Hindi, Hinglish, Tamil,
Bengali), it emits schema-exact JSON with an English summary,
translation, and action items carrying a confirmed | tentative
confidence tag — and returns an empty task list for purely conversational
notes instead of inventing tasks.
Part of the voice-note-digester project. A ready-to-run Q4_K_M GGUF is at yashwork-byte/gemma-3-4b-it-voice-digest-GGUF.
System prompt: see system_prompt.txt in this repo. User turn:
{instruction}
From: {sender}
{transcript}
Three trained instruction variants select the output format:
| Instruction | Output JSON |
|---|---|
Digest this voice note transcript. | {summary, translation, action_items} |
Digest this voice note transcript. Skip the translation. | {summary, action_items} (fast path) |
Translate this voice note transcript into English. | {translation} |
From: matters: the sender is app-known metadata fed into the model so
summaries can name the sender without guessing (training labels are grounded
in it).
68 gold notes (17 per language) run end-to-end: synthetic WhatsApp-style audio → IndicConformer-600M STT → this model (quantized Q4_K_M via llama.cpp
| Metric | Value |
|---|---|
| Action-item F1 (end-to-end) | 0.76 (bn 0.80 · hi 0.79 · ta 0.76 · hi-en 0.69) |
| Hallucinated-task rate on 20 pure-chat notes | 0.10 |
| Confidence-tag accuracy (matched items) | 0.90–1.00 |
| Translation chrF (end-to-end / clean text) | ~56 / ~64 |
| Schema-parse failures | 0 / 68 |
By phrasing style (end-to-end F1): imperative 0.86 · question 0.67 · colloquial 0.60 · implicit 0.25 (n=4, high variance).