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A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
A collection of token reduction (token pruning, merging, clustering, etc.) techniques for ML/AI
Dynamic context pruning plugin for OpenCode - intelligently manages conversation context to optimize token usage
Pi coding-agent extension for pruning tool-call trees
稳定可用Active Context Pruning — model-driven context management for OpenCode ，200K tokens is enough. ，主动压缩上下文，20w token足以
ADHD — a skill for coding agents. Tree-of-thought with pruning, built on the Claude & Codex Agent SDK. Fans out parallel divergent thoughts under different cognitive frames, scores, prunes traps, deepens the survivors. The no-brainer skill for creative and interdisciplinary work.
MLX port of moonshotai/Kimi-K3 (2.78T multimodal MoE): streaming converter, REAP expert pruning, and per-language expert-overlap analysis
稳定可用Active Context Pruning for pi ，100K tokens is enough. ，主动压缩上下文，10w 上下文足矣
Text-to-SQL access control: schema selection that gives the LLM only the tables the caller is allowed to read. Row-level-security-aware table pruning for LangChain, MCP and any SQL agent. Postgres, Oracle, MySQL, SQL Server. 75% fewer prompt tokens.