gemma-4-e2b-it-lite-mlx

gemma-4-e2b-it-lite-mlx

Text-only, lightweight 4-bit MLX quantization of google/gemma-4-e2b-it, optimized for on-device inference on iPhone (8GB devices) and Apple Silicon Macs.

~2.05 GB — compared to 3.58 GB for the official mlx-community/gemma-4-e2b-it-4bit (which bundles the audio/vision towers in BF16).

What was changed (modification notice)

• Converted from the original bf16 checkpoint with mlx-lm 0.31.3 (mlx_lm.convert).
• Text-only: audio_tower, vision_tower, embed_audio, embed_vision, multi_modal_projector weights are not included. This checkpoint works with text-only Gemma 4 runtimes (e.g. mlx-lm / mlx-swift-lm MLXLLM).
• Quantization recipe: 4-bit / group size 64 (affine) for all layers, except embed_tokens_per_layer (per-layer embeddings, ~1.3 GB at 4-bit) which is quantized to 2-bit / group size 64. The per-layer override is recorded in config.json (quantization section).
• KV-shared layers (15-34) do not include k_proj / v_proj / k_norm (same layout as the official MLX conversion).

Japanese smoke tests (greeting / Q&A / no repetition loops) show quality on par with the official 4-bit conversion on this recipe. A uniform 3-bit recipe of the same size was clearly worse and was rejected.

Attribution

Gemma 4 is developed by Google DeepMind and released under the Apache License 2.0. This repository redistributes a quantized derivative under the same license.

Usage (mlx-lm)

pip install mlx-lm
mlx_lm.generate --model mouri45/gemma-4-e2b-it-lite-mlx --prompt "こんにちは!"

Created as part of the AppleSiliconLLM project (Issue0012).