License & Attribution

This mirror redistributes Google DeepMind's Gemma 4 weights under Apache 2.0, the original license. All credit for the model goes to Google DeepMind.

• Original repo: https://huggingface.co/google/gemma-4-E2B-it
• License: Apache 2.0
• Authors: Google DeepMind

This is NOT a derivative work — every file in this repo is byte-identical to the canonical source as of the mirror date below. Use the canonical Google repo when available; this mirror exists as a redundancy.

Verification

All small files (config, tokenizer, README, etc.) were SHA-256 verified bit-identical to the canonical source at the time of upload. The 10.2 GB model.safetensors is content-addressed by HF LFS and the ETag matched.

How to use

This mirror is a drop-in replacement for the canonical repo:

python
Copy code
from transformers import AutoModelForMultimodalLM, AutoProcessor

# Either of these works:
model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-E2B-it")
model = AutoModelForMultimodalLM.from_pretrained("xaitalk/gemma-4-E2B-it-mirror")

Inside the xaitalk framework, xaitalk.hub.ensure_model("gemma-4-e2b-it") tries the canonical first and falls back to this mirror automatically.

Why xaitalk mirrors this model

xaitalk is a cross-framework XAI library that supports Gemma 4 in PyTorch, TensorFlow, and JAX with bit-equivalent attribution methods. Mirroring the canonical weights guarantees long-term reproducibility of the cross-framework benchmarks shipped with the library.

Cross-framework results for Gemma 4 E2B (8 methods × 2 modalities, fp32, A100 80GB, 2026-05-28):

Mirror policy

Per the xaitalk hub-mirror policy, third-party artifacts ≤ 2.5 GB are mirrored by default. Gemma 4 E2B at ~10 GB is an explicit exception because it's the flagship "any-to-any" thinking model in xaitalk's coverage matrix, and license compliance (Apache 2.0) permits redistribution with attribution.

— xaitalk