
Parameters	~12B dense
Modalities	Text, vision, audio (native in backbone)
License	Apache 2.0
Architecture	Encoder-free (no separate vision/audio towers)
Context	See upstream config
Vision in GGUF	Requires *`mmproj-.gguf`** alongside main weights

Why this repo exists

One download hub for all major quants (K-quants, IQ, Q8, mmproj).
Fast Hub-side sync from bartowski/gemma-4-12B-it-GGUF — no re-upload from your laptop.
Documented use cases for contributors: gemma-4-12b-local (agents, LiteRT, llama.cpp, MLX).
Apple Silicon MLX quants: Edmon02/gemma-4-12B-it-MLX

Available files

See gguf-manifest.json for the live file list.

Essential tier (recommended)

File	Use
`gemma-4-12B-it-Q4_K_M.gguf`	Best balance — 16 GB laptops
`gemma-4-12B-it-Q5_K_M.gguf`	Higher quality
`gemma-4-12B-it-Q6_K.gguf` / `Q8_0`	Max quality
`gemma-4-12B-it-Q3_K_M.gguf`	Tighter VRAM
`gemma-4-12B-it-Q2_K.gguf`	Minimum size
`gemma-4-12B-it-IQ4_XS.gguf` / `IQ4_NL.gguf`	IQ variants
`mmproj-gemma-4-12B-it-f16.gguf`	Required for images in llama.cpp

Full tier

All bartowski quants (Q2_K_L, Q3_K_XL, Q4_0, Q4_1, bf16, imatrix, etc.) — run make sync-gemma4-gguf-full.

Download

bash

pip install -U huggingface_hub

# Text + vision (recommended)
huggingface-cli download Edmon02/gemma-4-12B-it-GGUF \
  gemma-4-12B-it-Q4_K_M.gguf \
  mmproj-gemma-4-12B-it-f16.gguf \
  --local-dir ./models/gemma-4-12b

Accept the license on google/gemma-4-12B-it before using weights.

Quick start

llama.cpp (text)

llama-cli -m gemma-4-12B-it-Q4_K_M.gguf -p "Explain encoder-free multimodal models in 3 bullets." -n 256

llama.cpp (image + text)

bash

llama-mtmd-cli \
  -m gemma-4-12B-it-Q4_K_M.gguf \
  --mmproj mmproj-gemma-4-12B-it-f16.gguf \
  --image photo.jpg \
  -p "Describe this image."

LiteRT-LM (OpenAI-compatible local server)

bash

litert-lm import --from-huggingface-repo=litert-community/gemma-4-12B-it-litert-lm gemma-4-12B-it.litertlm gemma4-12b
litert-lm serve

LM Studio / Ollama

Import Edmon02/gemma-4-12B-it-GGUF and select Q4_K_M + mmproj.

Apple Silicon (MLX)

Native inference on M-series Macs via mlx-vlm (not mlx-lm — Gemma 4 is multimodal):

bash

pip install -U mlx-vlm
huggingface-cli download Edmon02/gemma-4-12B-it-MLX 4bit/ --local-dir ./models/gemma-4-12b-mlx
python -m mlx_vlm.generate --model ./models/gemma-4-12b-mlx/4bit --prompt "Hello" --max-tokens 128

See Edmon02/gemma-4-12B-it-MLX and make convert-gemma4-mlx for maintainer conversion.

Use cases

Use case	Quant	Tool
Local coding agent	Q4_K_M	OpenCode, Continue, Aider
Voice + vision assistant	Q5_K_M + mmproj	Google AI Edge Gallery / Eloquent (Mac)
Armenian + English research	Q4_K_M	Pair with HyVoxPopuli ASR/TTS
Low-VRAM laptop	Q3_K_M or IQ4_XS	llama.cpp
Fast inference	MTP drafter (upstream)	Google checkpoint + compatible runtime

Hardware guide

VRAM	Suggested files
8 GB	IQ4_XS or Q3_K_M (text only, short context)
16 GB	Q4_K_M + mmproj
24 GB+	Q6_K or Q8_0 + mmproj

Provenance

Item	Source
Base model	`google/gemma-4-12B-it`
GGUF quants	Mirrored from `bartowski/gemma-4-12B-it-GGUF`
Maintainer scripts	Edmon02/audio_set `scripts/sync_gemma4_gguf_quants.py`

Limitations

Community quants — validate quality on your tasks vs official BF16.
Audio in GGUF may require latest llama.cpp / LM Studio builds.
Gated upstream — HF token + license acceptance required for google/* repos.

Contributing

Add recipes under projects/gemma-4-12b-local/examples/. See CONTRIBUTING.md in that folder.

Citation

bibtex

@article{gemma_2026,
  title={Gemma 4},
  author={Google DeepMind},
  year={2026},
  url={https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12B/}
}