TaQuants/Gemma-4-12B-OBLITERATED-TaQuants-GGUF overview
Gemma 4 12B OBLITERATED TaQuants The Repository https://github.com/ek15072809/TaQuants Technical Report https://github.com/ek15072809/TaQuants/blob/main/docs/T…
Runs locally from ~5.48 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Gemma-4-12B-OBLITERATED-TaIQ3_S.gguf | GGUF | GGUF | 5.48 GB | Download |
Model Details
| Model ID | TaQuants/Gemma-4-12B-OBLITERATED-TaQuants-GGUF |
|---|---|
| Author | TaQuants |
| Pipeline | text-generation |
| License | — |
| Base model | OBLITERATUS/Gemma-4-12B-OBLITERATED |
| Last modified | 2026-07-03T10:49:59.000Z |
Model README
---
base_model:
- OBLITERATUS/Gemma-4-12B-OBLITERATED
pipeline_tag: text-generation
tags:
- TaQuants
- uncensored
- non-censored
- unfiltered
---
Gemma-4-12B-OBLITERATED TaQuants
The Tema_Q development team, team zenei, has developed a new importance matrix method called TaQuants (Tensor-aware Adaptive Quantization).
This model is a TaQuants version of OBLITERATUS/Gemma-4-12B-OBLITERATED created with TaQuants v3.0.
The model size and performance are as follows:
| | Size (GB) | Knowledge score |
| --- | --- | --- |
| Q4_K_M | 7.38 | 31.97 |
| TaIQ3_S | 5.48 | 31.88 |
| TaIQ2_M | | 9.09 |
Vulnerabilities to quantization were identified in the model. Q4_K_M caused partial collapse, while TaIQ2_M exhibited complete collapse. TaIQ3_S performs inference comparable to Q4_K_M but avoids the partial collapse observed with Q4_K_M.
Run TaQuants/Gemma-4-12B-OBLITERATED-TaQuants-GGUF with guIDE
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Source: Hugging Face · Compare models