This repository contains an MLX-ready QAT Q4 distribution of Google's Gemma 4 12B Unified instruction-tuned model, prepared for local inference on Apple Silicon Macs.
This is not a newly fine-tuned model. It is a quantized MLX redistribution converted from Google's official Quantization-Aware Training (QAT) unquantized checkpoint. No structural modifications or additional training have been applied to the model architecture.
Important Notice
This repository is hosted primarily as a dedicated engine source for the R.E.V.I.S. application ecosystem. You are free to download and use this model package for your own local MLX projects or workflows, subject to the Apache License 2.0 and Google's Gemma terms.
For the original model cards, architecture details, intended usage, limitations, and evaluation information, refer to the official upstream models:
Default language tensor quantization: MLX affine Q4
Group size: 64
Mode: affine
Token embeddings: MLX affine Q6
Audio, vision, and multimodal projection components: BF16
Norms, biases, and small non-linear tensors: preserved in their source floating-point representation
In practical terms, this is not a plain "quantize every linear layer to Q4" package. The embedding table is intentionally kept at higher precision, and the multimodal side of the model is not aggressively quantized. This keeps the MLX package closer to the official Google QAT Q4 distribution while still reducing local storage and memory requirements.
Optimized for R.E.V.I.S. (Local Cognitive OS)
We host and test this model package to serve as a local reasoning and judgment engine for R.E.V.I.S.
R.E.V.I.S. is a 100% local Cognitive OS for Multi-Agentic AI. It transforms your Mac devices into a distributed Agentic Swarm via zero-config Wi-Fi clustering, allowing you to run heavy AI workloads like recursive web research, dynamic RAG generation, and multi-step logic without killing single-machine performance.
If you are interested in pushing the limits of local AI and open-weight models, check out our project.
Gemma 4 12B Unified is a practical middle point between the smaller E2B / E4B models and the larger 26B MoE / 31B dense models. It is useful when local workflows need stronger reasoning and judgment while still remaining manageable on Apple Silicon systems with enough unified memory.
For lighter repeated routing or extraction checks, the E2B / E4B QAT Q4 variants may be faster. For heavier long-document reasoning, the 26B / 31B QAT Q4 variants may be more appropriate if your Mac has enough unified memory.
This repository redistributes a quantized derivative of Google's Gemma 4 12B instruction-tuned QAT release, which is distributed by Google under the Apache License 2.0.
This derivative is likewise distributed under the Apache License 2.0. A copy of the license is included in the LICENSE file in this repository, and can also be found at https://www.apache.org/licenses/LICENSE-2.0.
Modification Notice
Compared with the official Google QAT source checkpoint, this repository applies the following packaging modification:
The source checkpoint was converted to MLX format and quantized with a GGUF-like mixed Q4/Q6/BF16 policy for local MLX inference.
No fine-tuning, additional training, or architecture-level modification has been applied.
