gemma4-loghub-e2b-GGUF

gemma4-loghub-e2b-GGUF

Gemma 4 E2B fine-tuned for infrastructure, Linux, Windows, networking, cloud and application log analysis.

Files

gemma4-loghub-e2b-loghub-Q4_K_M.gguf

This is the recommended release artifact:

• Base family: Gemma 4 E2B
• Format: GGUF v3
• Quantization: Q4_K_M
• Mode: text-only language model
• Tensor count: 601
• Size: ~3.2 GB
• Chat template: embedded Gemma 4 template
• SHA256: e70b4d0009db2ec9fa4a57782d57264084572bb34bf9a4d41b76eb9559a07f73

Why Text-Only

Gemma 4 E2B is a multimodal model with language, audio and vision tensors. For log analysis we only need text inference. Some llama-server based tools fail when given a unified Gemma4 GGUF that contains the extra audio/vision tensors. This release keeps the fine-tuned language tensors and removes audio, vision and multimodal projector tensors.

llama.cpp

Use a recent llama.cpp build with Gemma4 support.

llama-server \
  -m gemma4-loghub-e2b-loghub-Q4_K_M.gguf \
  -c 4096 \
  --reasoning off

If your llama-server does not support --reasoning off, use:

llama-server \
  -m gemma4-loghub-e2b-loghub-Q4_K_M.gguf \
  -c 4096 \
  --chat-template-kwargs '{"enable_thinking":false}'

Ollama

From this directory:

ollama create gemma4-loghub-e2b:q4 -f Modelfile
ollama run gemma4-loghub-e2b:q4

Unsloth Studio

Import the real .gguf file directly:

gemma4-loghub-e2b-loghub-Q4_K_M.gguf

Do not import a symlink, the old LoRA adapter GGUF, or the unified multimodal GGUF.

Start with context length 2048 or 4096 on laptops.