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Gemma-4-E2B-it-MXFP4-GGUF
FreedomAISVR/Gemma-4-E2B-it-MXFP4-GGUF
Gemma-4-E2B-it-MXFP4-GGUF is a machine learning model from FreedomAISVR. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for llama.cpp. The card lists the license as apache-2.0.
- Model: google/gemma-4-E2B-it - Architecture: Gemma4ForConditionalGeneration (Vision + Text) - Parameters: ~3B (E2B = Efficient 2B-class) - Context Length: 131,072 tokens (128K) - License: Apache 2.0 - Update Date: J…
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.gguf4.3 GB · 100%
At a glance
- Library
- llama.cpp
- License
- apache-2.0
- Access
- Public
- Created
- Sep 16, 2026
- Updated
- Sep 16, 2026
- SHA
- a94b0ecc
Base models
Model card
From the Hugging Face model README
Gemma 4 E2B IT — MXFP4 GGUF
Base Model
- Model: google/gemma-4-E2B-it
- Architecture: Gemma4ForConditionalGeneration (Vision + Text)
- Parameters: ~3B (E2B = Efficient 2B-class)
- Context Length: 131,072 tokens (128K)
- License: Apache 2.0
- Update Date: July 20, 2026
Quantization Details
- Format: MXFP4 (OCP Microscaled FP4)
- BPW: 5.66 bits per weight
- File Size: 3.3 GB
- Non-expert tensors: F16
Vision Support
- mmproj:
mmproj-gemma-4-E2B-it-f16.gguf(985 MB, F16) - Supports image and video input
Performance (RTX 5060 Ti 16GB)
- Generation Speed: ~153 t/s at 128K context
- Context: 128K with Q8_0 KV cache fits in 16GB VRAM
Usage (llama.cpp)
llama-cli -m gemma-4-e2b-it-mxfp4.gguf -ngl 99 --flash-attn on -c 131072 --cache-type-k q8_0 --cache-type-v q8_0
Why No MTP?
The Gemma 4 E2B model does not include MTP (Multi-Token Prediction) heads.
Hardware Target
- NVIDIA RTX 50 series (Blackwell) for MXFP4 acceleration
- 16GB+ VRAM recommended
- Library
- llama.cpp
- License
- apache-2.0
- Created
- Sep 16, 2026
- Updated
- Sep 16, 2026