OsaurusAI/gemma-4-E2B-it-8bit

<p align="center"> <a href="https://osaurus.ai"><img src="https://cdn-avatars.huggingface.co/v1/production/uploads/69d00705ce8872981c6c4fce/GWKjOwezSOhW5iuKpDwq_.png" alt="Osaurus AI" width="120"></a> </p>

<h3 align="center">Gemma 4 E2B-it &mdash; 8-bit (MLX)</h3> <p align="center">Properly converted with all vision and audio tower weights verified intact</p>

<p align="center"> <a href="https://osaurus.ai"><img src="https://img.shields.io/badge/Web-osaurus.ai-blue" alt="Website"></a>&nbsp; <a href="https://huggingface.co/OsaurusAI"><img src="https://img.shields.io/badge/HF-OsaurusAI-yellow?logo=huggingface" alt="OsaurusAI"></a> </p>

Why this exists: Some mlx-community conversions of Gemma 4 have broken or zeroed-out vision/audio tower weights, producing models that appear functional for text but silently fail on image and audio inputs. This is a clean conversion from the original google/gemma-4-E2B-it with every multimodal weight tensor verified non-zero.

Model Details

Multimodal Weight Verification

Every tensor in every multimodal component was loaded and checked for max(abs(tensor)) > 0. Zero broken weights found.

Usage

# Requires Osaurus (https://osaurus.ai)
osaurus serve OsaurusAI/gemma-4-E2B-it-8bit

# Python API
from mlx_vlm import load, generate

model, processor = load("OsaurusAI/gemma-4-E2B-it-8bit")

# Text-only
output = generate(model, processor, "Explain quantum computing", max_tokens=500)

# With image
output = generate(model, processor, "Describe this image", ["path/to/image.jpg"], max_tokens=500)

Conversion Details

<p align="center">Converted by <a href="https://osaurus.ai">Osaurus AI</a></p>