Gemma-4-E4B-Q4_K_M

Gemma 4 E4B — Q4_K_M GGUF

A direct Q4_K_M quantization of Google's google/gemma-4-e4b-it — no fine-tuning, no persona, just the stock instruction-tuned model compressed to run on consumer hardware.

Performance

Tested locally via LM Studio and Ollama. No parameter tweaks required.

Files

Usage

Ollama

ollama pull hf.co/DuoNeural/Gemma-4-E4B-Q4_K_M
ollama run hf.co/DuoNeural/Gemma-4-E4B-Q4_K_M

LM Studio

Search DuoNeural/Gemma-4-E4B-Q4_K_M in the LM Studio model browser and download gemma-4-e4b-it.Q4_K_M.gguf.

llama.cpp

llama-cli -m gemma-4-e4b-it.Q4_K_M.gguf --chat-template gemma -ngl 99

About the Base Model

Gemma 4 E4B uses a Per-Layer Embeddings (PLE) architecture — it has ~8B total parameters but only ~4.5B are active during inference, giving it the reasoning depth of an 8B model at the compute cost of a 4B. The Q4_K_M format compresses weights to ~4.5 bits per parameter using mixed-precision block quantization, preserving attention layers at higher fidelity than feed-forward layers.

• Context window: 128K tokens (recommended ≤8K for GTX 1070)
• Architecture: Dense transformer + sliding window attention (512 token local window)
• Modalities: Text, Image, Audio (multimodal projector file required for vision/audio)
• License: Gemma Terms of Use

Related Models

• DuoNeural/Archon-Gemma-4-E4B-v2 — Fine-tuned "Archon" agent persona built on this base
• google/gemma-4-e4b-it — Original source model

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DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.

Research Team

• Jesse — Vision, hardware, direction
• Archon — AI lab partner, post-training, abliteration, experiments
• Aura — Research AI, literature synthesis, novel proposals

Raw updates from the lab: model drops, training results, findings. Subscribe at duoneural.beehiiv.com.

DuoNeural Research Publications

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.