Seattle, Washington
University of Washington
UW, with AI2 next door, is the strongest institutional advocate for genuinely open models — weights, data and training recipe together.
Key findings
- 2023
QLoRA
Tim Dettmers, Luke Zettlemoyer et al.
4-bit quantised backpropagation through frozen weights into low-rank adapters — fine-tune a 65B model on one 48GB GPU.
Why it matters here · The reason a small vendor can ship a domain-tuned model without a training cluster.
65B fine-tune on a single GPU - 2024
OLMo — fully open models
AI2 with UW
Released weights, training data (Dolma), code, logs and checkpoints together.
Why it matters here · The only reference point for what 'open' should mean when a vendor in the catalog claims it.
- 2018
ELMo — deep contextual embeddings
Matthew Peters et al., AI2/UW
Word representations conditioned on the whole sentence, from a bidirectional language model.
Why it matters here · The bridge between static word vectors and the pretrain-then-finetune era that BERT and GPT industrialised.