grug-27b

grug-27b grug get big brain now. 27B brain. biggest grug yet that actually work. normal model think like this: "Okay, so the user wants me to carefully consider the best approach here. Let me think about this step by step..." grug think like this: "Need parse date. Regex or datetime. datetime.strptime safer. Handle bad input, return None." same reasoning. same depth. way fewer token. grug throw grammar padding in fire, keep all brain meat. answer come out normal english, full quality. only inside voice is grug. what grug-27b is **base**: Qwen/Qwen3.6-27B (apache 2.0) **method**: LoRA r=32 on all text-stack linears, merged to bf16 **loss**: think-only loss on trajectory data (grug never imitate old seed english say-word), full loss on fresh high-quality data **data**: grug-think-v3-10k agent trajectories + fresh gpt-5.5 set (hard math derivations, hard code design, chat, tool sessions) with dense adaptive grug think grug think short when task small. grug think LONG when task hard. length earned by real constraint, never by "carefully thinking" smoke. why 27b not break like 35b 35b brother repeat words until cave fall down. grug study why. train data had think in every history turn. agent tool (OpenCode etc) throw old think away. model see world it never train on. model panic. model loop. grug-27b train on BOTH world: 1. think-in-history trajectories (framework keep think) 2. stripped-history variants: old think gone, exactly like agent framework replay, only final turn trained then grug run repetition stress gauntlet before release: greedy long-form generation, deep think-stripped agent replay, multi-turn continuation. numbers below. no loop, no repeat sickness. see difference same HumanEval problem (`separate_paren_groups`), both solve correct: Qwen3.6-27B think: 6,539 tokens.** start like this and keep going for pages: The user wants a Python function `separate_paren_groups` that takes a string of parentheses and spaces, and returns a list of strings. Each string i