⚡ All JANG models are meant to be run in vMLX
JANGQ-AI/gemma-4-E4B-it-qat-JANG_4M
JANG_4M MLX affine bundle converted from google/gemma-4-E4B-it-qat-q4_0-unquantized.
This bundle keeps Gemma 4 bookends and media-sensitive components coherent: token embeddings/output projection, norms, media towers/embedders, and Gemma 4 per-layer embedding/gate/projector tensors are fp16 passthrough; self-attention and MoE router projections are 8-bit affine; decoder MLP/expert bulk is 4-bit affine.
Bundle
| Field | Value |
|---|---|
| Source | google/gemma-4-E4B-it-qat-q4_0-unquantized |
| Architecture | gemma4 / Gemma4ForConditionalGeneration |
| Text layers | 42 |
| Hidden size | 2560 |
| Weight format | jang_affine |
| Top-level quantization | bits=8, group_size=32, mode=affine |
| Tier bits | attention=8, router=8, mlp=4, embed=16, per_layer_media=16 |
| Quantized modules | 259 affine bases with .scales and .biases sidecars |
| Shards | 6 safetensors shards |
| Capabilities | multimodal |
Modalities
| Modality | Status |
|---|---|
| Text | supported |
| Vision | source config present and preserved |
| Audio | audio_config is present and preserved. |
| Video | No video_config is present, so this card does not claim a verified video runtime path. |
Runtime support depends on a Gemma 4 compatible MLX/vMLX loader that understands config.json quantization overrides, jang_config.json, and Gemma 4 processor/chat-template files.
Runtime Metadata
config.jsonhas source-derivedhas_vision,has_audio,has_video,modalities, andcapabilities.tokenizer_config.jsonincludesbos_token_id,eos_token_id,pad_token_id, and the patched Gemma 4 chat template.processor_config.jsonis preserved for Gemma 4 multimodal processing.- MTP/speculative drafter weights are not present in the source checkpoint; metadata is
mtp: none/mtp_policy: none.