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cstr/gemma4-e4b-it-GGUF overview

Gemma 4 E4B it — GGUF GGUF conversion of google/gemma 4 E4B it https://huggingface.co/google/gemma 4 E4B it for use with CrispStrobe/CrispASR https://github.co…

ggmlggufaudiospeech-recognitiongemmaconformerautomatic-speech-recognitionenmultilingualbase_model:google/gemma-4-E4B-itbase_model:quantized:google/gemma-4-E4B-itlicense:apache-2.0region:us

Runs locally from ~4.11 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
243
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Pipeline
automatic-speech-recognition
Author

Repository Files & Downloads

3 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
gemma4-e4b-it-f16.ggufGGUFF1614.58 GBDownload
gemma4-e4b-it-q4_k.ggufGGUFQ4_K4.11 GBDownload
gemma4-e4b-it-q8_0.ggufGGUFQ8_07.75 GBDownload

Model Details

Model IDcstr/gemma4-e4b-it-GGUF
Authorcstr
Pipelineautomatic-speech-recognition
Licenseapache-2.0
Base modelgoogle/gemma-4-E4B-it
Last modified2026-08-02T15:23:09.000Z

Model README

---

license: apache-2.0

language:

  • en
  • multilingual

pipeline_tag: automatic-speech-recognition

tags:

  • audio
  • speech-recognition
  • gguf
  • gemma
  • conformer

library_name: ggml

base_model: google/gemma-4-E4B-it

---

Gemma-4-E4B-it — GGUF

GGUF conversion of google/gemma-4-E4B-it for use with CrispStrobe/CrispASR.

E4B is the larger sibling of cstr/gemma4-e2b-it-GGUF: the same gemma4 architecture (byte-identical 1024-dim USM Conformer audio tower) with a larger decoder. It runs on the same CrispASR backend with no code changes.

> The Gemma-4 12B model is a different architecture (gemma4_unified, 640-dim audio encoder) and is not supported by this backend.

Available variants

| File | Quant | Size | Notes |

|---|---|---|---|

| gemma4-e4b-it-f16.gguf | F16 | ~14.6 GB | Full precision |

| gemma4-e4b-it-q8_0.gguf | Q8_0 | ~7.8 GB | Near-lossless quant |

| gemma4-e4b-it-q4_k.gguf | Q4_K | ~4.1 GB | Standard quant (auto-download default) |

Model details

  • Architecture: USM Conformer audio encoder (12L, 1024d, chunked-local attention with relative position bias, LightConv1d, ClippableLinear with QAT scalars) + Gemma4 LLM decoder (42L, 2560d, GQA 8Q/2KV, per-layer embeddings, hybrid sliding/full attention, GeGLU)
  • Parameters: ~4B effective (~8B with embeddings)
  • Audio: Gemma4AudioFeatureExtractor — 128-bin mel, 16 kHz, frame_length=320, hop=160, fft_length=512, semicausal padding, log(mel + mel_floor=0.001), no normalisation
  • Languages: 140+ (ASR + speech translation)
  • License: Apache 2.0
  • Source: google/gemma-4-E4B-it

Usage

# Auto-download the Q4_K default and transcribe:
crispasr -m gemma4-e4b --auto-download -f audio.wav

# Or point at a local file (backend auto-detected from the GGUF):
crispasr --backend gemma4-e2b -m gemma4-e4b-it-q4_k.gguf -f audio.wav

The CrispASR backend is named gemma4-e2b (it serves the whole gemma4 E2B/E4B family).

What's included vs an upstream Gemma-4 GGUF

This GGUF is built specifically for ASR with CrispASR and includes the audio path that standard text/vision Gemma-4 GGUFs omit:

  • 12-layer audio conformer encoder.
  • Gemma4MultimodalEmbedder audio→LLM adapter (embed_audio.embedding_projection, pre-projection RMSNorm).
  • Mel filterbank + Hann window baked into the GGUF.

Converted with models/convert-gemma4-e2b-to-gguf.py.

Provenance and EU AI Act Art. 53 note

  • Upstream model: google/gemma-4-E4B-it — published by google.
  • Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
  • What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
  • Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
  • Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.

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