Sage-A1.5 is a 35B mixture-of-experts model based on Qwen3.6 for scientific reasoning, coding, and tool use. This official Q4_K_M GGUF build uses llama.cpp and includes the vision projector.
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Sage-A1.5 is a 35B mixture-of-experts model based on Qwen3.6 for scientific reasoning, coding, and tool use. This official Q8_0 GGUF build uses llama.cpp and includes the vision projector.
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Tess 4.5 is a 27B Qwen3.8 fine-tune for coding, reasoning, and tool use. This Q4_K_M GGUF build includes the F16 vision projector and uses llama.cpp with the embedded chat template. Speculative decoding is disabled.
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Tess 4.5 is a 27B Qwen3.8 fine-tune for coding, reasoning, and tool use. This Q6_K GGUF build includes the F16 vision projector and uses llama.cpp with the embedded chat template. Speculative decoding is disabled.
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Tess 4.5 is a 27B Qwen3.8 fine-tune for coding, reasoning, and tool use. This Q8_0 GGUF build includes the F16 vision projector and uses llama.cpp with the embedded chat template. Speculative decoding is disabled.
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Spark-X2.6-4B is XHToken's 4B text model for conversation, reasoning, coding, and multilingual tasks. This build uses Q4_K_M GGUF weights, the embedded Jinja chat template, and a 32K-token default context.
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Spark-X2.6-4B is XHToken's 4B text model for conversation, reasoning, coding, and multilingual tasks. This build uses Q8_0 GGUF weights, the embedded Jinja chat template, and a 32K-token default context.
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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.
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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.
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Repository: localaiLicense: mit
# DeepSeek-V4.1-Flash-1017 Technical Report 👁️ ## Introduction **DeepSeek-V4.1-Flash-1017** is a minor update to **DeepSeek-V4.1-Flash**, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses. *† denotes the text-only subset of HLE. We use the Claude Code harness for Terminal-Bench 4.0 and the Mini-SWE-V2 harness for DeepSWE v1.1.* ## Prompt Encoding This release does not include a Jinja-format chat template. The `encoding` folder contains a self-contained Python reference implementation (`encoding.py`) with test cases for multi-turn conversations, tool calling, thinking mode, numeric reasoning effort, mid-conversation system messages, and interleaved image content. ...
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Repository: localaiLicense: apache-2.0

Qwen3.8-27B in cdiamond's iMatrix-calibrated mixed-precision NVFP4 build. Selected attention and feed-forward tensors retain higher precision. Includes the F16 vision projector and enables the embedded MTP draft head with one draft token per step.
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Kujira's abliterated adaptation of Underdog Saluki 27B 1.0 uses mixed IQ2 weights with an embedded MTP head. This text-only llama.cpp build uses ordinary decoding; the MTP variant enables speculative decoding.
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GLM-5.3-Flash is a mixture-of-experts model for reasoning, coding, tool use, and vision. Maya-S v2 mixes IQ2_XXS and IQ2_S experts with Q6_K attention and shared experts.
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GLM-5.3-Flash is a mixture-of-experts model for reasoning, coding, tool use, and vision. Maya-S24 uses mixed 2-bit experts with Q4_K attention and shared experts.
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GLM-5.3-Flash is a mixture-of-experts model for reasoning, coding, tool use, and vision. Maya-M uses mixed 2-bit and 3-bit experts with Q6_K attention and shared experts.
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GLM-5.3-Flash is a mixture-of-experts model for reasoning, coding, tool use, and vision. Maya-L uses mixed 3-bit and 4-bit experts with higher precision in sensitive layers.
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Simplicio-27B is a Qwen3.8-27B fine-tune for code editing and software engineering. This Q4_K_M GGUF build uses llama.cpp and includes the F16 vision projector.
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Simplicio-27B is a Qwen3.8-27B fine-tune for code editing and software engineering. This Q8_0 GGUF build uses llama.cpp and includes the F16 vision projector.
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Repository: localaiLicense: swift-open-license-1.0
Swift-Qwen3.8-27B is UkisAI's reasoning-efficient fine-tune of Qwen3.8-27B. The publisher reports 58.3% fewer thinking tokens with less than 1% quality loss. This Q4_K_M GGUF includes the F16 vision projector and enables MTP speculative decoding. The weights use the Swift Open License v1.0.
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Cyber-Ornith 1.5 is a 9B Qwen3.5 fine-tune for security auditing, terminal tasks, and tool use. This importance-matrix Q4_K_M GGUF build uses llama.cpp with the embedded chat template for text chat.
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Cyber-Ornith 1.5 is a 9B Qwen3.5 fine-tune for security auditing, terminal tasks, and tool use. This importance-matrix Q6_K GGUF build uses llama.cpp with the embedded chat template for text chat.
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