---

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-E2B-it

---

Gemma-4-E2B-it — GGUF

GGUF conversion of google/gemma-4-E2B-it for use with CrispStrobe/CrispASR.

Available variants

| File | Quant | Size | Notes |

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

| gemma4-e2b-it.gguf | F16 | ~9.5 GB | Full precision |

| gemma4-e2b-it-q8_0.gguf | Q8_0 | ~5.0 GB | Near-lossless quant |

| gemma4-e2b-it-q4_k.gguf | Q4_K | ~2.8 GB | Standard quant |

| gemma4-e2b-it-q2_k.gguf | Q2_K | ~2.2 GB | Smallest, quality drop |

Model details

• Architecture: USM Conformer audio encoder (12L, 1024d, chunked-local attention with relative position bias, LightConv1d, ClippableLinear with QAT scalars) + Gemma4 LLM decoder (35L, 1536d, GQA 8Q/1KV, per-layer embeddings, hybrid sliding/full attention, GeGLU)
• Parameters: 2.3B effective (5.1B 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-E2B-it

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 (unsloth, ggml-org) omit:

• 12-layer audio conformer encoder (~872 tensors total).
• Gemma4MultimodalEmbedder audio→LLM adapter (embed_audio.embedding_projection,

pre-projection RMSNorm).

• All ClippableLinear QAT clipping scalars (.input_min/max, .output_min/max) — these

are NOT QAT-only artefacts. HF applies them at inference via

Gemma4ClippableLinear.forward. Skipping them collapses the encoder past layer 5.

• num_kv_shared_layers, layer_full_mask, partial_rotary_factor,

global_head_dim, use_double_wide_mlp, attention_k_eq_v — all the per-layer

flags the LLM forward needs to honour.

• Mel filterbank + Hann window resources (HTK no-norm filters,

frame_length=320 window; the runtime regenerates these too).

Vision tower tensors are excluded.

Usage with CrispASR

# Auto-download (recommended)
./build/bin/crispasr --backend gemma4-e2b -m auto --auto-download -f audio.wav

# Or explicit path
./build/bin/crispasr --backend gemma4-e2b -m gemma4-e2b-it-q4_k.gguf -f audio.wav

Differential testing

CrispASR ships a stage-by-stage differential test against the HF PyTorch

reference. Per-stage cosine similarity vs HF Gemma4AudioModel:

mel_spectrogram          1.0000   bit-exact (HF FE faithfully reproduced)
audio_subsample_output   0.9994   conv2d + LayerNorm + ReLU
audio_layer_0..11        0.97 — 0.99 (with QAT clip scalars)
audio_tower_output       0.99+

Run it yourself:

# 1. Dump HF reference
HF_HOME=/path/to/hf-cache python tools/dump_reference.py \
    --backend gemma4 --model-dir google/gemma-4-E2B-it \
    --audio samples/jfk.wav --output /tmp/gemma4-ref.gguf

# 2. Compare
build/bin/crispasr-diff gemma4 \
    gemma4-e2b-it-q4_k.gguf /tmp/gemma4-ref.gguf samples/jfk.wav

Conversion provenance

This GGUF was produced by models/convert-gemma4-e2b-to-gguf.py (CrispASR repo)

running on Kaggle T4 nodes (16 GB RAM). Conversion config:

• --outtype f16 then crispasr-quantize for Q-variants.
• ClippableLinear QAT scalars persisted as 1-element F32 tensors named

audio.layers.{i}.{linear}.input_min/max, output_min/max.

• Vision tower (model.vision_tower., model.embed_vision.) skipped.

Provenance and EU AI Act Art. 53 note

• Upstream model: google/gemma-4-E2B-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.