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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

gemma-4-e2b-it-artemis-q3-k-l
TurinTech Artemis Q3_K_L quantization of Gemma 4 E2B from Google's QAT checkpoint, for local chat and tool calls. Accepts image input through the bundled F16 vision projector. Uses the embedded chat template and an 8192-token context. Thinking is on by default.

Repository: localaiLicense: apache-2.0

gemma-4-e2b-it-artemis-q3-k-m
TurinTech Artemis Q3_K_M quantization of Gemma 4 E2B from Google's QAT checkpoint, for local chat and tool calls. Accepts image input through the bundled F16 vision projector. Uses the embedded chat template and an 8192-token context. Thinking is on by default.

Repository: localaiLicense: apache-2.0

gemma-4-e2b-it-artemis-iq3-xxs
TurinTech Artemis IQ3_XXS quantization of Gemma 4 E2B from Google's QAT checkpoint, for local chat and tool calls. Accepts image input through the bundled F16 vision projector. Uses the embedded chat template and an 8192-token context. Thinking is on by default.

Repository: localaiLicense: apache-2.0

gemma-4-e2b-it-artemis-iq2-m
TurinTech Artemis IQ2_M quantization of Gemma 4 E2B from Google's QAT checkpoint, for local chat and tool calls. Accepts image input through the bundled F16 vision projector. Uses the embedded chat template and an 8192-token context. Thinking is on by default.

Repository: localaiLicense: apache-2.0

qwen3.5-2b-artemis-q5-k-m
TurinTech Artemis Q5_K_M quantization of Qwen3.5 2B for local tool calls, maths, and instruction following. Accepts image input through the bundled F16 vision projector. Uses the embedded chat template and an 8192-token context.

Repository: localaiLicense: apache-2.0

qwen3.5-2b-artemis-q4-k-m
TurinTech Artemis Q4_K_M quantization of Qwen3.5 2B for local tool calls, maths, and instruction following. Accepts image input through the bundled F16 vision projector. Uses the embedded chat template and an 8192-token context.

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

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