---library_name:transformersbase_model:google/gemma-4-E2B-itlicense:gemmatags:-gemma-4-audio-merged-unsloth-speech-lisper---# Lisper Gemma 4 E2B Audio Full Checkpoint
This is the merged Lisper checkpoint: `google/gemma-4-E2B-it` with the trained Lisper LoRA adapter folded into a standalone `safetensors` model.
## Model Lineage- Base model: `google/gemma-4-E2B-it`- Training: Unsloth supervised fine-tuning with QLoRA / LoRA
- Merge method: base + LoRA adapter merged into a 16-bit checkpoint
- Weight file: `model.safetensors`- Training rows: `16,000`- Validation rows: `2,000`- Held-out test rows: `2,000`
This is not a dense full-parameter fine-tune. It is a merged base+LoRA checkpoint for easier deployment.
## Evaluation
The release-quality evaluation is the v18 hybrid acoustic+Gemma path:
- Held-out rows: `2,000`- Hard errors: `0`- Verdict: `pass`- Class match: `0.976`- Clear/non-clear match: `0.989`- Exact four-line format: `1.0`- Reason/cue/encouragement present: `1.0`
The evaluated pipeline uses acoustic features for the lisp-class hint and Gemma for structured coaching text and tone. Do not interpret these metrics as a pure direct-Gemma raw-audio classification result.
## Deployment- Browser/WebGPU demo: `thomasjvu/lisper-gemma4-e2b-audio-onnx-q4f16`- Server-side ZeroGPU fallback: `thomasjvu/lisper-zerogpu`- Adapter-only package: `thomasjvu/lisper-gemma4-e2b-audio-lora`
Use the q4f16 ONNX/WebGPU package for browser demos. Use this merged checkpoint as the server-side correctness reference.
## Limitations- The lisp dataset is synthetically generated from speaker-disjoint source speech.
- This is a practice assistant, not a medical diagnosis tool or a replacement for a speech-language pathologist.
