These weights are compressed-tensors (pack-quantized / int-quantized). Do not deploy via TGI “text-generation-inference” paths — that backend does not load our CT format and produces opaque worker/load errors. INT4 post-training quantization of google/gemma-4-e2b-it — Google's 2B-active multimodal MoE with 128 experts. The smallest capable multimodal MoE. Runs on any 8 GB GPU. Quantized with datafree RTN (QuantizationModifier) — AutoRound (blocked — see KNOWN-FAILURES) wrapper blocked by Gemma4 qproj.linear children (see KNOWN-FAILURES). Temporary path until upstream fixed. The LLM backbone is W4A16; vision tower, projector, and PLE layers remain BF16. embedtokensperlayer and…
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88plug
88plug
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