AI Model2026
An uncensored fork of Qwen3.8-27B that removes refusal/safety filters via an “abliteration” technique while preserving the first 15 layers and multimodal capabilities; intended for controlled research and testing rather than production.
AI Model2026
Local GGUF build of Qwen 3.8 27B with the refusal direction ablated for llama.cpp; includes vision projector (mmproj), MTP speculative head, a 262k context window and multiple quant tiers (Q2–Q8, F16). Research-only release that requires updated llama.cpp and explicit safety layers.
AI Model2026
An uncensored, "abliterated" fork of Qwen3.8-27B that removes refusal behavior by modifying targeted weights and provides multiple GGUF/BF16 quantized variants for local research and deployment, while carrying significantly reduced safety filtering.
Enables interactive serving of large Mixture-of-Experts (MoE) models on personal machines by adapting offload and execution to measured device bandwidth and agentic workload patterns. Key features include bandwidth-adaptive execution, semantic-aware caching of recurrent state, and an elastic GPU expert cache; supports 20+ MoE models and runs models from ~35B to 753B on consumer/workstation GPUs.
AI Agent Papers2026
Enables closed-loop execution for embodied agents by evolving code-based runtime critics and recovery skills online while keeping the base policy frozen. Combines three timescale loops with Z-Infra rollout infrastructure; reports 90.8% on LIBERO-Pro, 93.6% on RoboCasa and an 11.1× inference speedup.