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tess-4.5-27b-q4
Tess 4.5 is a 27B Qwen3.8 fine-tune for coding, reasoning, and tool use.
This Q4_K_M GGUF build includes the F16 vision projector and uses
llama.cpp with the embedded chat template. Speculative decoding is disabled.
Repository: localaiLicense: apache-2.0
tess-4.5-27b-q6
Tess 4.5 is a 27B Qwen3.8 fine-tune for coding, reasoning, and tool use.
This Q6_K GGUF build includes the F16 vision projector and uses
llama.cpp with the embedded chat template. Speculative decoding is disabled.
Repository: localaiLicense: apache-2.0
tess-4.5-27b-q8
Tess 4.5 is a 27B Qwen3.8 fine-tune for coding, reasoning, and tool use.
This Q8_0 GGUF build includes the F16 vision projector and uses
llama.cpp with the embedded chat template. Speculative decoding is disabled.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-2b
Ternary Bonsai 2 2B is a vision-language model derived from Qwen3.5-2B
with ternary transformer weights. This PQ2_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-2b-ptq1
Ternary Bonsai 2 2B is a vision-language model derived from Qwen3.5-2B
with ternary transformer weights. This PTQ1_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-2b-q2
Ternary Bonsai 2 2B is a vision-language model derived from Qwen3.5-2B
with ternary transformer weights. This Q2_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-9b
Ternary Bonsai 2 9B is a vision-language model derived from Qwen3.5-9B
with ternary transformer weights. This PQ2_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-9b-ptq1
Ternary Bonsai 2 9B is a vision-language model derived from Qwen3.5-9B
with ternary transformer weights. This PTQ1_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-9b-q2
Ternary Bonsai 2 9B is a vision-language model derived from Qwen3.5-9B
with ternary transformer weights. This Q2_0 build uses the Bonsai
backend and includes the Q8_0 vision projector. The three standard
packings contain the same weights in different storage layouts.
Repository: localaiLicense: apache-2.0
qwen3.8-27b-imatrix-nvfp4-mtp
Qwen3.8-27B in cdiamond's iMatrix-calibrated mixed-precision NVFP4
build. Selected attention and feed-forward tensors retain higher
precision. Includes the F16 vision projector and enables the embedded
MTP draft head with one draft token per step.
Repository: localaiLicense: apache-2.0
atomic-minicpm-v-4.7-35b-a3b-uncensored-q4
AtomicChat's abliterated MiniCPM-V 4.7 is a 35B mixture-of-experts model
with 3B active parameters for chat and image understanding. This
AD-Q4_K-IQ4_XS build includes all 11 weight shards and the F16 vision
projector, with the embedded chat template and a 32K-token default context.
Repository: localaiLicense: apache-2.0
atomic-minicpm-v-4.7-35b-a3b-uncensored-q6
AtomicChat's abliterated MiniCPM-V 4.7 is a 35B mixture-of-experts model
with 3B active parameters for chat and image understanding. This
AD-Q6_K build includes all 16 weight shards and the F16 vision
projector, with the embedded chat template and a 32K-token default context.
Repository: localaiLicense: apache-2.0
huihui-qwen3.8-flash-next-abliterated-v2-q4
Huihui's second abliterated Qwen3.8-Flash-Next release adjusts the
abliteration without modifying the experts. This vision-language
mixture-of-experts model uses the publisher's UD-Q4_K_XL GGUF
and BF16 projector for text chat and image input through llama.cpp.
The default context is 32,768 tokens. Model weights use the
Qwen Community License 1.0.
Repository: localaiLicense: other
huihui-qwen3.8-flash-next-abliterated-v2-iq4
Huihui's second abliterated Qwen3.8-Flash-Next release adjusts the
abliteration without modifying the experts. This vision-language
mixture-of-experts model uses the publisher's UD-IQ4_XS GGUF
and BF16 projector for text chat and image input through llama.cpp.
The default context is 32,768 tokens. Model weights use the
Qwen Community License 1.0.
Repository: localaiLicense: other
glm-5.3-flash-maya-s
GLM-5.3-Flash is a mixture-of-experts model for reasoning, coding, tool use, and vision. Maya-S v2 mixes IQ2_XXS and IQ2_S experts with Q6_K attention and shared experts.
Repository: localaiLicense: mit
glm-5.3-flash-maya-s24
GLM-5.3-Flash is a mixture-of-experts model for reasoning, coding, tool use, and vision. Maya-S24 uses mixed 2-bit experts with Q4_K attention and shared experts.
Repository: localaiLicense: mit
glm-5.3-flash-maya-m
GLM-5.3-Flash is a mixture-of-experts model for reasoning, coding, tool use, and vision. Maya-M uses mixed 2-bit and 3-bit experts with Q6_K attention and shared experts.
Repository: localaiLicense: mit
glm-5.3-flash-maya-l
GLM-5.3-Flash is a mixture-of-experts model for reasoning, coding, tool use, and vision. Maya-L uses mixed 3-bit and 4-bit experts with higher precision in sensitive layers.
Repository: localaiLicense: mit
simplicio-27b-q4
Simplicio-27B is a Qwen3.8-27B fine-tune for code editing and software engineering.
This Q4_K_M GGUF build uses llama.cpp and includes the F16 vision projector.
Repository: localaiLicense: apache-2.0
simplicio-27b-q8
Simplicio-27B is a Qwen3.8-27B fine-tune for code editing and software engineering.
This Q8_0 GGUF build uses llama.cpp and includes the F16 vision projector.
Repository: localaiLicense: apache-2.0
ternary-bonsai-2-27b
Ternary Bonsai 2 27B (PrismML) is a 27B-class reasoning model with ternary
transformer weights. This PTQ1_0 build packs the trits densely at 1.75 bits
per weight (5.95 GB) and includes the Q8_0 vision projector. PTQ1_0 is a
Prism-private GGUF type, so the entry uses the bonsai backend (PrismML's
llama.cpp fork) instead of stock llama.cpp.
Repository: localaiLicense: apache-2.0
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