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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-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

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. The linked variant uses the higher-quality Q8_0 quantization.

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

ling-3.0-flash-iq1
Ling-3.0-flash is InclusionAI's MIT-licensed hybrid reasoning MoE model with 124B total parameters and 5.5B active parameters per token. It targets coding, deep research, instruction following, and agentic workflows with a native 256K-token context window. This default entry uses the 36.5 GB AD-IQ1_M GGUF. A higher-quality 44.7 GB AD-IQ2_XS model is available as a variant.

Repository: localaiLicense: mit

ling-3.0-flash-iq2
Ling-3.0-flash in the higher-quality 44.7 GB AD-IQ2_XS GGUF format. This variant preserves more model fidelity for hosts with enough memory.

Repository: localaiLicense: mit

ornith-1.5-35b-a3b-q4
Ornith-1.5-35B-A3B is an MIT-licensed Qwen3.5 mixture-of-experts model from Ornith AI for agentic coding, reasoning, repository-level software tasks, and tool use. It activates about 3B parameters per token and supports text and image input with a context window of 262K tokens. This default entry uses the Q4_K_M GGUF and BF16 vision projector. A higher-quality Q8_0 model is available as a variant.

Repository: localaiLicense: mit

ornith-1.5-35b-a3b-q8
Ornith-1.5-35B-A3B in the higher-quality Q8_0 GGUF format, with the shared BF16 vision projector for multimodal prompts.

Repository: localaiLicense: mit

thomson-1.0-small-q4
Thomson-1.0-Small is a 35B-parameter mixture-of-experts model with about 3B active parameters. It focuses on legal, tax, journalism, research, reasoning, tool use, and document processing. It supports text and image input with a native context window of 262K tokens. This default entry uses the Q4_K_M GGUF and BF16 vision projector. A higher-quality Q8_0 model is available as a variant.

Repository: localaiLicense: polyform-strict-1.0.0

thomson-1.0-small-q8
Thomson-1.0-Small in the higher-quality Q8_0 GGUF format, with the shared BF16 vision projector for multimodal prompts.

Repository: localaiLicense: polyform-strict-1.0.0

tiel-coder-35b-a3b-q4
Tiel-Coder-35B-A3B is a 35B-parameter mixture-of-experts model for coding, reasoning, tool use, and vision tasks. This default entry uses the Q4_K_XL GGUF and BF16 vision projector.

Repository: localaiLicense: mit

tiel-coder-35b-a3b-q4-mtp
Tiel-Coder-35B-A3B in Q4_K_XL format with MTP speculative decoding and a BF16 vision projector.

Repository: localaiLicense: mit

tiel-coder-35b-a3b-q8
Tiel-Coder-35B-A3B in the higher-quality Q8_K_XL GGUF format, with the BF16 vision projector for multimodal prompts.

Repository: localaiLicense: mit

ornith-1.5-397b-q4
Ornith-1.5-397B is Ornith AI's MIT-licensed flagship mixture-of-experts model for agentic coding, reasoning, repository-level tasks, and tool use. It supports text and image input with a context window of 262K tokens. This default entry uses the Q4_K_M GGUF and BF16 vision projector. A higher-quality Q8_0 model is available as a variant.

Repository: localaiLicense: mit

ornith-1.5-397b-q8
Ornith-1.5-397B in the higher-quality Q8_0 GGUF format, with the shared BF16 vision projector for multimodal prompts.

Repository: localaiLicense: mit

btl-4-compact
BTL-4 Compact is Bad Theory Labs' text-only 35B mixture-of-experts model compressed into a single 9.96 GB IQ2_XXS GGUF. Around 2.1B parameters are active per token, and the model is tuned for agentic work, tool use, coding, and reasoning. The compact build omits the vision tower and disables the source model's MTP layer for compatibility with stock llama.cpp.

Repository: localaiLicense: apache-2.0

deepseek-v4-pro-0813
DeepSeek V4 Pro 0813 is DeepSeek's MIT-licensed flagship mixture-of-experts model for agentic coding, reasoning, and long-horizon tool use. This entry uses Unsloth's UD-Q4_K_XL GGUF build, split into 20 shards for llama.cpp.

Repository: localaiLicense: mit

instella-moe-16b-a3b-think
AMD Instella-MoE-16B-A3B-Think is a reasoning and instruction-following mixture-of-experts model with 16 billion total parameters and 3 billion active parameters. It supports long-form reasoning, chat, coding, and tool use. This entry uses the Q4_K_M GGUF quantization.

Repository: localaiLicense: other

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