drex-v1.5-GGUF

drex v1.5 is a ~9B-parameter decision model from Nace.AI, built on a Qwen3_5ForCausalLM hybrid-attention backbone (32 layers, with a 3:1 ratio of linear-attention to full-attention layers) distilled from XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B, paired with a dedicated pointer head that scores options directly from hidden states rather than generating text. Instead of producing free-form output, it takes a state (text or JSON) plus typed questions—choice, noul (yes/no), or ordinal score—and returns calibrated probabilities over the possible answers in a single forward pass per question, supporting context lengths from 16,384 up to 131,072 tokens. It communicates via the TypeSafe-compatible System One API (POST /v1/systemone), making it a drop-in replacement for Kev- or Jev-format decision servers, and can be run locally through the bundled Python inference/serving scripts, llama.cpp, or Ollama (with bf16 or Q8_0 GGUF weights), or accessed via Nace's hosted Drex API. Despite its relatively small size, it ranks as the top model under 10B parameters on the public Decision Index 0.3.1 (58.08), performs competitively with the larger/undisclosed Jev 1.13.0 on JevBench accuracy (86.2% vs 87.0%) and game-arena win rate (56.8%), and maintains strong accuracy (89–93%) even on long-context documents up to 128k tokens.

Model Files

File Name Quant Type File Size File Link Description
drex-v1.5.BF16.gguf BF16 17.9 GB Link Full BF16 weights. Highest quality, largest file size.
drex-v1.5.Q3_K_L.gguf Q3_K_L 4.93 GB Link Lower quality but usable, good for low RAM availability.
drex-v1.5.Q3_K_M.gguf Q3_K_M 4.62 GB Link Low quality.
drex-v1.5.Q4_K_M.gguf Q4_K_M 5.63 GB Link Good quality, default size for most use cases, recommended.
drex-v1.5.Q4_K_S.gguf Q4_K_S 5.35 GB Link Slightly lower quality with more space savings, recommended.
drex-v1.5.Q5_K_M.gguf Q5_K_M 6.47 GB Link High quality, recommended.
drex-v1.5.Q5_K_S.gguf Q5_K_S 6.31 GB Link High quality, recommended.
drex-v1.5.Q6_K.gguf Q6_K 7.36 GB Link Very high quality, near perfect, recommended.
drex-v1.5.Q8_0.gguf Q8_0 9.53 GB Link Extremely high quality, generally unneeded but max available quant.

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp

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