ModelVerificadoExterno

Tema Q R 4B TaQuants GGUF

The TemaQ 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/TemaQ-R-4B created with...

Fontehuggingface.co/TaQuants/Tema_Q-R-4B-TaQuants-GGUF/tree/9f2b15c228d88d6942e7f248b59c10017cc10bcb

TipoOtherModelo basetemaq-org/Tema_Q-R-4BVersãoRevision 9f2b15cPublicador@temaq-orgCnota

Fonte do modelo

Tipo: Other
Tamanho do arquivo: 1,43–1,96 GB
Modelo base: temaq-org/Tema_Q-R-4B
Versão: vRevision 9f2b15c
Atualizado: 3 de out. de 2026
Publicador: @temaq-org
Downloads: 49
Parâmetros: 4B
Tarefas: Geração de texto
Fonte: Hugging Face

Descrição da fonte

Tema_Q-R-4B 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-R-4B created with TaQuants v2.0.

The model size and performance are as follows:
TaIQ2_M is 0.01GB compressed and shows a 0.96% improvement in PPL compared to IQ2_M. TaIQ3_S has a file size increase of 0.16GB compared to IQ3_S. On the other hand, it shows a 3.43% improvement in PPL compared to Q4_K_M, which is 0.35GB larger.

Fontes

1 fonte

Hugging FacePrimárioVersão 9f2b15c228d88d6942e7f248b59c10017cc10bcb

huggingface.co/TaQuants/Tema_Q-R-4B-TaQuants-GGUF/tree/9f2b15c228d88d6942e7f248b59c10017cc10bcb

Verificado 3 de out.

Artefatos de modelo

2 artefatos

Tema_Q-R-4B-TaIQ2_M.gguf

gguf · 1,43 GB · SHA-256 34e46a180c7d…8ad4 · Hugging Face

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Tema_Q-R-4B-TaIQ3_S.gguf

gguf · 1,96 GB · SHA-256 10268f410ee7…da08 · Hugging Face

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Trechos de fonte

3 trechos

Readme11 de jul.huggingface.co/TaQuants/Tema_Q-R-4B-TaQuants-GGUF

--- base_model: - temaq-org/Tema_Q-R-4B pipeline_tag: text-generation tags: - TaQuants - uncensored - non-censored - unfiltered --- # Tema_Q-R-4B TaQuants [The Repository](https://github.com/ek15072809/TaQuants) [Technical Report](https://github.com/ek15072809/TaQuants/blob/main/docs/TaQuants_Technical_Report.pdf) 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-R-4B](https://huggingface.co/temaq-org/Tema_Q-R-4B) created with TaQuants v2.0. The model size and performance are as follows: TaIQ2_M is 0.01GB compressed and shows a 0.96% improvement in PPL compared to IQ2_M. TaIQ3_S has a file size increase of 0.16GB compared to IQ3_S. On the other hand, it shows a 3.43% improvement in PPL compared to Q4_K_M, which is 0.35GB larger.

Note3 de out.huggingface.co/TaQuants/Tema_Q-R-4B-TaQuants-GGUF

Source context: 49 downloads · 0 likes · Pipeline text-generation · Repo TaQuants/Tema_Q-R-4B-TaQuants-GGUF

Note17 de jul.huggingface.co/TaQuants/Tema_Q-R-4B-TaQuants-GGUF

Source context: 344 downloads · 0 likes · Pipeline text-generation · Repo TaQuants/Tema_Q-R-4B-TaQuants-GGUF