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ornith-1.5-122b-ud-q4-k-xl
Ornith 1.5 122B is a Qwen3.5 mixture-of-experts model for reasoning,
coding, and tool use. This UD-Q4_K_XL GGUF build includes the F16 vision
projector for image input and uses an 8,192-token default context.
Repository: localaiLicense: mit
ornith-1.5-122b-q8
Ornith 1.5 122B is a Qwen3.5 mixture-of-experts model for reasoning,
coding, and tool use. This Q8_0 GGUF build includes the F16 vision
projector for image input and uses an 8,192-token default context.
Repository: localaiLicense: mit
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
apodex-1.1-mini-q4
Apodex-1.1-mini is an Apache-2.0 Qwen3.5 mixture-of-experts model for
long-horizon research, data analysis, coding, file work, and tool use. It
activates about 3B of its 35.95B parameters per token and supports text and
image input with a context window of 262K tokens.
This default entry uses the recommended Q4_K_M GGUF and F16 vision
projector. An MTP-enabled build and a higher-quality Q8_0 model are
available as variants.
Repository: localaiLicense: apache-2.0
apodex-1.1-mini-q4-mtp
Apodex-1.1-mini with MTP speculative decoding enabled on the recommended
Q4_K_M GGUF. The model carries its native MTP head, so it needs no separate
draft model. The F16 vision projector supports multimodal prompts.
Repository: localaiLicense: apache-2.0
apodex-1.1-mini-q8
Apodex-1.1-mini in the higher-quality Q8_0 GGUF format, with the shared F16
vision projector for multimodal prompts.
Repository: localaiLicense: apache-2.0
glm-5.3-flash-q4
GLM-5.3-Flash is Z.ai's natively multimodal 320B-parameter mixture-of-experts
model with 18B active parameters. It combines sparse and linear attention
for coding, agentic work, tool use, vision, and long-context tasks. This
entry uses the UD-Q4_K_XL GGUF quantization and enables the model's MTP
speculative-decoding head.
Repository: localaiLicense: mit
glm-5.3-flash-q8
GLM-5.3-Flash is Z.ai's natively multimodal 320B-parameter mixture-of-experts
model with 18B active parameters. It combines sparse and linear attention
for coding, agentic work, tool use, vision, and long-context tasks. This
entry uses the higher-quality Q8_0 GGUF quantization and enables the model's
MTP speculative-decoding head.
Repository: localaiLicense: mit
pocket-darwin-180b-q4
POCKET-Darwin-180B is a text-only reasoning model based on
Qwen3.8-Flash-Next. This UD-Q4_K_XL GGUF build uses llama.cpp and
downloads all four shards (about 111.3 GB). It uses the embedded
chat template, an 8192-token default context, and the publisher's
recommended sampling settings. Weights use Qwen Community License 1.0.
Repository: localaiLicense: other
huihui-qwen3.8-flash-next-abliterated-q4
Huihui's abliterated Qwen3.8-Flash-Next is a vision-language
mixture-of-experts model modified to reduce refusals. This entry uses
the publisher's UD-Q4_K_XL GGUF and BF16 vision 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
qwen3.8-flash-next-q4
Qwen3.8-Flash-Next is Qwen's 125B-parameter, 6B-active experimental
vision-language mixture-of-experts model. It targets agentic coding,
reasoning, tool use, and long-context workloads with a native 262K-token
context window.
This default entry uses Unsloth's UD-Q4_K_XL GGUF and BF16 vision
projector. Linked variants offer Q8_0 and AtomicChat's smaller IQ4_XS
and Q4_K_M builds with a separate n-gram table shard.
Repository: localaiLicense: other
qwen3.8-flash-next-q8
Qwen3.8-Flash-Next in the higher-quality Q8_0 GGUF format, with the shared
BF16 vision projector. This build preserves more model quality but needs
more memory than the default Q4 variant.
Repository: localaiLicense: other
qwen3.8-flash-next-turbo-brilliance-iq3-s
DavidAU's Turbo Brilliance build of Qwen3.8 Flash Next uses GSQ-RCO
IQ3_S mixed quantization and the publisher's embedded chat template.
Includes both GGUF shards and the BF16 vision projector for text chat
and image input, with a 32K default context. See the model card for
the publisher's reasoning and instruction modes.
Repository: localaiLicense: other
qwen3.8-flash-next-gsq-rco-q2-0
Qwen3.8 Flash Next with ISTA DASLab's GSQ-RCO Q2_0 mixed quantization.
Includes both GGUF shards (transformer weights and n-gram embeddings)
and the BF16 vision projector for text chat and image input.
Uses llama.cpp with the embedded chat template and a 32K context.
Repository: localaiLicense: other
qwen3.8-flash-next-gsq-rco-iq2-xs
Qwen3.8 Flash Next with ISTA DASLab's GSQ-RCO IQ2_XS mixed quantization.
Includes both GGUF shards (transformer weights and n-gram embeddings)
and the BF16 vision projector for text chat and image input.
Uses llama.cpp with the embedded chat template and a 32K context.
Repository: localaiLicense: other
qwen3.8-flash-next-gsq-rco-iq3-xxs
Qwen3.8 Flash Next with ISTA DASLab's GSQ-RCO IQ3_XXS mixed quantization.
Includes both GGUF shards (transformer weights and n-gram embeddings)
and the BF16 vision projector for text chat and image input.
Uses llama.cpp with the embedded chat template and a 32K context.
Repository: localaiLicense: other
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