Discover and install AI models from our curated collection

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.
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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.
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Qwopus3.8-27B-Flash-V2 is a new post-training release for reasoning and agent workloads. This Q4_K_M GGUF includes the F32 vision projector and uses llama.cpp's embedded chat template with MTP speculative decoding.
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Qwopus3.8-27B-Flash-V2 is a new post-training release for reasoning and agent workloads. This Q8_0 GGUF includes the F32 vision projector and uses llama.cpp's embedded chat template with MTP speculative decoding.
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Repository: localaiLicense: apache-2.0

Qwopus3.8-27B-Flash is a Qwen3.8-27B fine-tune for reasoning and agent workloads. This Q4_K_M GGUF includes the F32 vision projector and uses llama.cpp's embedded chat template with MTP speculative decoding. The publisher reports a known Python code indentation issue.
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Repository: localaiLicense: apache-2.0

Qwopus3.8-27B-Flash is a Qwen3.8-27B fine-tune for reasoning and agent workloads. This Q8_0 GGUF includes the F32 vision projector and uses llama.cpp's embedded chat template with MTP speculative decoding. The publisher reports a known Python code indentation issue.
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DavidAU's Jumbo Fusion edition of Qwen3.8-27B merges community fine-tunes and includes a custom template with reasoning and instruction modes. This Q4_K_M GGUF build supports text chat with a 32,768-token default context.
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DavidAU's Jumbo Fusion edition of Qwen3.8-27B merges community fine-tunes and includes a custom template with reasoning and instruction modes. This Q8_0 GGUF build supports text chat with a 32,768-token default context.
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Repository: localaiLicense: apache-2.0
DavidAU's Turbo Brilliance 50X edition of Qwen3.8-27B includes a custom chat template with selectable reasoning and instruction modes. This Q4_K_M GGUF build includes the F16 vision projector for image input. Uses the embedded template with a 32,768-token context by default.
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Repository: localaiLicense: apache-2.0
DavidAU's Turbo Brilliance 50X edition of Qwen3.8-27B includes a custom chat template with selectable reasoning and instruction modes. This Q8_0 GGUF build includes the F16 vision projector for image input. Uses the embedded template with a 32,768-token context by default.
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Repository: localaiLicense: apache-2.0
Supra2-100M-Instruct is a compact English chat model trained from scratch by SupraLabs on the Qwen3 architecture. It has 100 million parameters, a 2,048-token context window, and is intended for lightweight experiments and constrained edge deployments. This entry uses the publisher's official F16 GGUF build.
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Repository: localaiLicense: apache-2.0
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.
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Repository: localaiLicense: other

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.
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Repository: localaiLicense: apache-2.0
DFM Mimir is an Apache-2.0, instruction-tuned HRM-Text model from Danish Foundation Models. It has about 1 billion parameters and a 4,096-token context window. The model focuses on Danish and English chat, reasoning, mathematics, and code generation, and uses only permissible post-training data. This entry serves the official BF16 safetensors checkpoint with vLLM.
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Repository: localaiLicense: mit
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.
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Repository: localaiLicense: apache-2.0
Hy-MT2-7B is Tencent's 7B multilingual translation model. It supports translation instructions across 33 languages, including terminology control and style transfer. This Q4_K_M GGUF uses the embedded chat template with an 8K context window. Include the target language in your prompt.
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Repository: localaiLicense: apache-2.0
Hy-MT2-7B is Tencent's 7B multilingual translation model. It supports translation instructions across 33 languages, including terminology control and style transfer. This Q6_K GGUF uses the embedded chat template with an 8K context window. Include the target language in your prompt.
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Repository: localaiLicense: apache-2.0
Hy-MT2-7B is Tencent's 7B multilingual translation model. It supports translation instructions across 33 languages, including terminology control and style transfer. This Q8_0 GGUF uses the embedded chat template with an 8K context window. Include the target language in your prompt.
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Repository: localaiLicense: apache-2.0
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.
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Repository: localaiLicense: mit
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.
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Repository: localaiLicense: apache-2.0
Spark-X2.5-4B Heretic JP is a Japanese adaptation of Spark-X2.5-4B with continued pretraining, instruction tuning, and tool-call training. This Q4_K_M GGUF uses the embedded chat template and an 8K-token default context. Allow at least 1024 output tokens for thinking and the answer.
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