Model Detail
Gemma-4-12B-OBLITERATED
▲ 1.7%Gemma-4-12B-OBLITERATED is a large language model with 12B parameters released by OBLITERATUS. The model is registered under the text-generation pipeline tag on Hugging Face, released under the gemma license.
Gemma-4-12B-OBLITERATED ships with 12B parameters. Total weight footprint is approximately 12.0 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the gemma license — review the exact terms before commercial deployment.
Downloads of Gemma-4-12B-OBLITERATED have moved +1.7% over the past 24 hours, +60.2% over the trailing seven days. That puts the model in active uptrend territory; a sustained move of this size usually reflects a recent release, a viral integration, or a benchmark surprise rather than steady-state demand. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
Gemma-4-12B-OBLITERATED is best fit for general-purpose chat and instruction-following workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Gemma 4 Technical Report
arXiv:2607.02770v2 Announce Type: replace Abstract: We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures,
Do Active SAE Feature Planes Carry More Holonomy? A Preregistered Reversal in Gemma
arXiv:2607.20522v1 Announce Type: new Abstract: This paper tests whether holonomy concentrates on active sparse-autoencoder (SAE) feature planes in Gemma 2 2B, a concrete operationalization of the broader semantic-concentration prediction. Holonomy is measured at the final-token layer-12 to layer-13
Trivial Prompt Reframing Bypasses Safety Guardrails in Google\'s MedGemma-4B
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Hugging Face and Cerebras bring Gemma 4 to real-time voice AI
DistilledGemma: Balanced Efficiency-Accuracy for Person-Place Relation Extraction from Multilingual Historical Articles
arXiv:2606.29130v1 Announce Type: new Abstract: We present DistilledGemma, an efficient and accurate system for the HIPE-2026 shared task on person-place relation extraction from multilingual historical newspaper articles in English, German, and French. Our approach adopts a three-stage knowledge di
How Transparent is DiffusionGemma?
arXiv:2606.20560v1 Announce Type: cross Abstract: LLM reasoning transparency is a critical affordance for understanding model decisions, mitigating misuse and misalignment, and debugging surprising model behaviors. However, DiffusionGemma performs a larger fraction of its computation in a continuous
