Gemma 4 E2B It Mirror

--- library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: - google/gemma-4-E2B-it tags: - gemma4 - mirror - multimodal - xaitalk --- # xaitalk/gemma-4-E2B-it-mirror **Bit-identical mirror of [`google/gemma-4-E2B-it`](https://huggingface.co/google/gemma-4-E2B-it).** Maintained by the [xaitalk](https://github.com/xaitalk/xaitalk) project as a fallback source so library users can always download Gemma 4 E2B even if the canonical Google repo becomes temporarily unavailable. ## 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](https://ai.google.dev/gemma/docs/gemma_4_license) - 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. | Field | Value | |---|---| | Canonical | `google/gemma-4-E2B-it` | | Mirror | `xaitalk/gemma-4-E2B-it-mirror` | | Mirror date | 2026-05-28 | | Total size | ~10.3 GB | | Files mirrored | 9 (all) | ## How to use This mirror is a drop-in replacement for the canonical repo: ```python 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](https://github.com/xaitalk/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. C...