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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-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-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

s1-mini-q4
S1-mini by Superwhisper is a 0.6B English text normalizer for raw speech transcripts. It removes fillers and false starts, restores punctuation and capitalization, and formats spoken numbers, dates, currency, and email addresses as written text. This default entry uses the publisher's 462 MB Q4_K_M GGUF and greedy decoding. A higher-fidelity F16 model is available as a variant. Prefix the transcript with the styling, structure, and context control line documented on the model page.

Repository: localaiLicense: s1-mini-license

llm-jp-4-33b-thinking-q4
LLM-jp-4-33B-thinking is an Apache-2.0 Japanese and English reasoning model from Japan's National Institute of Informatics. Its dense Llama architecture has 33 billion parameters and a 65K-token context window. The model was aligned with supervised fine-tuning and DPO for multi-turn conversation and instruction following. This default entry uses the 20.2 GB Q4_K_M GGUF. The official 66.4 GB BF16 weights are available as a higher-fidelity variant.

Repository: localaiLicense: apache-2.0

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

ling-3.0-tiny-q4
Ling-3.0-tiny is InclusionAI's MIT-licensed hybrid reasoning MoE model with 7.9B total parameters and 1.3B active parameters per token. It targets reasoning, coding, instruction following, and agentic tasks with a native 131K-token context window. This default entry uses the Q4_K_M GGUF. A higher-quality Q8_0 model is available as a variant.

Repository: localaiLicense: mit

ling-3.0-tiny-q8
Ling-3.0-tiny in the higher-quality Q8_0 GGUF format. This variant preserves more model fidelity for hosts with enough memory.

Repository: localaiLicense: mit

granite-4.2-3b-q4
IBM Granite 4.2 3B is a compact multilingual reasoning model for chat, coding, long-context tasks, and tool use. This entry uses the Q4_K_M GGUF; a higher-fidelity Q8_0 build is available as a variant.

Repository: localaiLicense: apache-2.0

granite-4.2-3b-q8
IBM Granite 4.2 3B in the higher-fidelity Q8_0 GGUF format. It is a compact multilingual reasoning model for chat, coding, and tool use.

Repository: localaiLicense: apache-2.0

granite-4.2-8b-q4
IBM Granite 4.2 8B is a multilingual reasoning model for chat, coding, long-context tasks, and tool use. This entry uses the Q4_K_M GGUF; a higher-fidelity Q8_0 build is available as a variant.

Repository: localaiLicense: apache-2.0

granite-4.2-8b-q8
IBM Granite 4.2 8B in the higher-fidelity Q8_0 GGUF format. It is a multilingual reasoning model for chat, coding, and tool use.

Repository: localaiLicense: apache-2.0

granite-4.2-30b-q4
IBM Granite 4.2 30B is the family's flagship multilingual reasoning model for chat, coding, long-context tasks, and tool use. This entry uses the Q4_K_M GGUF; a higher-fidelity Q8_0 build is available as a variant.

Repository: localaiLicense: apache-2.0

granite-4.2-30b-q8
IBM Granite 4.2 30B in the higher-fidelity Q8_0 GGUF format. It is the family's flagship multilingual reasoning model for chat, coding, and tool use.

Repository: localaiLicense: apache-2.0

dirk-qwen3.8-27b-q8
Dirk in the higher-quality Q8_K_XL GGUF format, with MTP speculative decoding and the shared F16 vision projector for multimodal prompts.

Repository: localaiLicense: apache-2.0

dirk-qwen3.8-27b-q5
Dirk in the higher-quality Q5_K_XL GGUF format, with MTP speculative decoding and the shared F16 vision projector for multimodal prompts.

Repository: localaiLicense: apache-2.0

dirk-qwen3.8-27b-q6
Dirk in the higher-quality Q6_K_XL GGUF format, with MTP speculative decoding and the shared F16 vision projector for multimodal prompts.

Repository: localaiLicense: apache-2.0

qwen3.8-27b-uncensored-q4
Qwen3.8-27B-Uncensored reduces refusal behavior while retaining the base model's text, vision, reasoning, and tool-use capabilities. Its integrated MTP head supports speculative decoding without a separate draft model. This default entry uses the Q4_K_M GGUF and F16 vision projector. A higher-quality Q8_0 model is available as a variant.

Repository: localaiLicense: apache-2.0

qwen3.8-27b-uncensored-q8
Qwen3.8-27B-Uncensored in the higher-quality Q8_0 GGUF format, with its integrated MTP head and shared F16 vision projector.

Repository: localaiLicense: apache-2.0

hy-mt2-1.8b-q4
Hy-MT2-1.8B is Tencent's compact multilingual translation model. It follows translation instructions across 33 languages and supports tasks such as terminology control, style transfer, and structure-preserving translation. This default entry uses the 1.1 GB Q4_K_M GGUF. A higher-quality Q8_0 model is available as a variant.

Repository: localaiLicense: apache-2.0

hy-mt2-1.8b-q8
Hy-MT2-1.8B in the higher-quality 1.9 GB Q8_0 GGUF format. This variant preserves more model fidelity for hosts with enough memory.

Repository: localaiLicense: apache-2.0

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