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TaQuants/Tema_Q-X4-8B-Thinking-TaQuants-GGUF overview

Tema Q X4 8B Thinking TaQuants The Repository https://github.com/ek15072809/TaQuants Technical Report https://github.com/ek15072809/TaQuants/blob/main/docs/TaQ…

ggufTaQuantsuncensorednon-censoredunfilteredtext-generationbase_model:temaq-org/Tema_Q-X4-8B-Thinkingbase_model:quantized:temaq-org/Tema_Q-X4-8B-Thinkingendpoints_compatibleregion:usimatrixconversational

Runs locally from ~4.19 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation
Author

Repository Files & Downloads

2 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Tema_Q-X4-8B-Thinking-TaIQ3_S.ggufGGUFGGUF4.44 GBDownload
Tema_Q-X4-8B-Thinking-TaQ2_K.ggufGGUFGGUF4.19 GBDownload

Model Details

Model IDTaQuants/Tema_Q-X4-8B-Thinking-TaQuants-GGUF
AuthorTaQuants
Pipelinetext-generation
License
Base modeltemaq-org/Tema_Q-X4-8B-Thinking
Last modified2026-07-03T10:47:20.000Z

Model README

---

base_model:

  • temaq-org/Tema_Q-X4-8B-Thinking

pipeline_tag: text-generation

tags:

  • TaQuants
  • uncensored
  • non-censored
  • unfiltered

---

Tema_Q-X4-8B-Thinking TaQuants

The Repository

Technical Report

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 temaq-org/Tema_Q-X4-8B-Thinking created with TaQuants v2.9.

The model size and performance are as follows:

| | Size (GB) | Knowledge score |

| --- | --- | --- |

| Q4_K_S | 5.20 | 31.79 |

| TaQ2_K | 4.50 | 36.31 |

| TaIQ3_S | 5.76 | 40.96 |

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