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lfm2.5-24b-a2b-turbo-brilliance-iq4-xs
DavidAU's Turbo Brilliance build of LFM2.5-24B-A2B, a text-only reasoning
model with 24B total parameters and 2.45B active parameters. This entry
uses the IQ4_XS GGUF quantization and the embedded Turbo Brilliance
chat template for multilingual chat and tool use.
Repository: localaiLicense: other
lfm2.5-24b-a2b-turbo-brilliance-q8
DavidAU's Turbo Brilliance build of LFM2.5-24B-A2B, a text-only reasoning
model with 24B total parameters and 2.45B active parameters. This entry
uses the Q8_0 MAX GGUF quantization and the embedded Turbo Brilliance
chat template for multilingual chat and tool use.
Repository: localaiLicense: other
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
humanizer-12b-bf16
Humanizer is a 12B Gemma 4 fine-tune for rewriting English and Chinese text.
This unquantized BF16 GGUF build uses llama.cpp with an 8,192-token context.
This entry uses the completions API with the publisher's exact rewriting prompt.
Repository: localaiLicense: apache-2.0
lfm2.5-24b-a2b-ud-q4
LiquidAI LFM2.5-24B-A2B is a text-only reasoning model with 24B total
parameters and 2.45B active parameters for multilingual chat and tool use.
This entry uses Unsloth's dynamic UD-Q4_K_XL GGUF quantization.
Repository: localaiLicense: other
lfm2.5-24b-a2b-ud-q6
LiquidAI LFM2.5-24B-A2B is a text-only reasoning model with 24B total
parameters and 2.45B active parameters for multilingual chat and tool use.
This entry uses Unsloth's dynamic UD-Q6_K_XL GGUF quantization.
Repository: localaiLicense: other
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
atomic-lfm2.5-24b-a2b-uncensored
AtomicChat's LFM2.5-24B-A2B derivative reduces refusal behavior in the
original weights. This text-only mixture-of-experts model has 24B total
parameters and supports multilingual reasoning and tool use.
This entry uses the AD-Q4_K_M GGUF build with the edits baked in.
Repository: localaiLicense: other
atomic-lfm2.5-24b-a2b-uncensored-q6
AtomicChat's LFM2.5-24B-A2B derivative reduces refusal behavior in the
original weights. This text-only mixture-of-experts model has 24B total
parameters and supports multilingual reasoning and tool use.
This entry uses the AD-Q6_K GGUF build with the edits baked in.
Repository: localaiLicense: other
atomic-lfm2.5-24b-a2b-uncensored-q8
AtomicChat's LFM2.5-24B-A2B derivative reduces refusal behavior in the
original weights. This text-only mixture-of-experts model has 24B total
parameters and supports multilingual reasoning and tool use.
This entry uses the AD-Q8_0 GGUF build with the edits baked in.
Repository: localaiLicense: other
Attention: Trust Remote Code is required for this model
wemm-embedding-2b
WeMM-Embedding-2B is Tencent's Apache-2.0 multilingual embedding model
built on Qwen3.5. This entry serves the original bfloat16 safetensors with
LocalAI's Transformers backend and produces 2,048-dimensional normalized
embeddings for text retrieval, semantic search, and RAG.
The upstream model can also embed images and videos. LocalAI currently
exposes text input through its embeddings API for this backend.
Repository: localaiLicense: apache-2.0
qwen3.8-2b-q4
Qwen3.8-2B is Empero AI's smallest Qwen3.8 reasoning distillation. It uses
the Qwen3.5-2B architecture and targets mathematics, instruction
following, tool use, and edge deployment with a native 262K-token context
window. This default entry uses Q4_K_M weights; a higher-quality Q8_0
build is available as a variant.
Repository: localaiLicense: apache-2.0
qwen3.8-2b-q8
Qwen3.8-2B in the higher-quality Q8_0 GGUF format. This variant preserves
more model fidelity while remaining suitable for compact hosts.
Repository: localaiLicense: apache-2.0
qwen3.8-2b-distill-q4
Qwen3.8 2B Distill is an Apache-2.0, text-only Qwen3.5 2B fine-tune
distilled from Qwen3.8 2.4T A95B reasoning traces. It targets compact
reasoning, coding, instruction following, and function calling with a 262K
native context window. This entry uses the balanced Q4_K_M GGUF
quantization; the Q8_0 variant offers higher fidelity.
Repository: localaiLicense: apache-2.0
qwen3.8-2b-distill-q8
Qwen3.8 2B Distill in the higher-fidelity Q8_0 GGUF format. This text-only
Qwen3.5 2B fine-tune targets reasoning, coding, instruction following, and
function calling with a 262K native context window.
Repository: localaiLicense: apache-2.0
mellum2.1-12b-a2.5b-thinking
Mellum2.1 Thinking is a JetBrains mixture-of-experts reasoning model with
12 billion total parameters and 2.5 billion active parameters per token.
It emits reasoning in think blocks and supports a 131,072-token context.
This entry uses Q4_K_M GGUF weights with a 32,768-token default context.
Repository: localaiLicense: apache-2.0
mellum2.1-12b-a2.5b-thinking-q8
Mellum2.1 Thinking is a JetBrains mixture-of-experts reasoning model with
12 billion total parameters and 2.5 billion active parameters per token.
It emits reasoning in think blocks and supports a 131,072-token context.
This entry uses Q8_0 GGUF weights with a 32,768-token default context.
Repository: localaiLicense: apache-2.0
mellum2-12b-a2.5b-instruct
Mellum2-12B-A2.5B-Instruct is an Apache-2.0 mixture-of-experts model from
JetBrains with 12 billion total parameters, 2.5 billion activated per token,
and a 131,072-token context window.
This entry uses the Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
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