Project Greenhouse pre-trained a 3B model from scratch on 8 H100s and fine-tuned it into a pointwise reranker that beats comparable open-weight baselines on TREC DL and BEIR, trailing only GPT-6.1 Sol
Project Greenhouse models LLM training as a directed property hypergraph and uses it to define what makes a model fully open and sovereign; as a first milestone, the authors pre-train a 3.29B-parameter causal language model from scratch on ClimbMix using only publicly available datasets, then fine-tune it on RLHN-250K with localized contrastive estimation to obtain the pointwise reranker Gaggle (base reranker). Its mean nDCG@10 exceeds all compared fine-tuned pointwise and listwise rerankers on TREC DL 2019–2023 and seven BEIR collections, with only GPT-6.1 Sol scoring higher on TREC DL.