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Demonstrates deep dream effects on modern LLMs by optimizing images to maximize probability of a target caption; Gemma 12B (no vision encoder) stamps recognizable objects, while E4B (with vision encoder) drifts to texture.
A developer benchmarks Gemma 4 E4B using Google's LiteRT engine against a Q4 GGUF quant, finding ~2.4x speedup in text generation due to multi-token prediction (MTP), but only 1.1x in image captioning. The post provides a Python wrapper for an OpenAI-compatible endpoint, though with limitations like deterministic output and single-session engine.
The author proposes a method to add the E4B audio encoder to larger models by extracting the encoder, creating a linear projection layer, and fine-tuning only that layer with text-audio pairs, similar to a referenced paper but using Gemma instead of Whisper.