{"work":{"id":"87bfa84a-e663-4165-806f-93ef439d88d0","openalex_id":null,"doi":null,"arxiv_id":"2308.01390","raw_key":null,"title":"OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models","authors":null,"authors_text":"Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu","year":2023,"venue":"cs.CV","abstract":"We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replication of DeepMind's Flamingo models. On seven vision-language datasets, OpenFlamingo models average between 80 - 89% of corresponding Flamingo performance. This technical report describes our models, training data, hyperparameters, and evaluation suite. We share our models and code at https://github.com/mlfoundations/open_flamingo.","external_url":"https://arxiv.org/abs/2308.01390","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-20T10:43:12.432476+00:00","pith_arxiv_id":"2308.01390","created_at":"2026-05-09T19:05:10.197751+00:00","updated_at":"2026-05-20T10:43:12.432476+00:00","title_quality_ok":true,"display_title":"OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models","render_title":"OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models"},"hub":{"state":{"work_id":"87bfa84a-e663-4165-806f-93ef439d88d0","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":49,"external_cited_by_count":null,"distinct_field_count":7,"first_pith_cited_at":"2023-05-05T17:59:46+00:00","last_pith_cited_at":"2026-05-18T13:54:04+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-05-20T17:21:58.609601+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":15},{"context_role":"baseline","n":5},{"context_role":"method","n":1}],"polarity_counts":[{"context_polarity":"background","n":15},{"context_polarity":"baseline","n":5},{"context_polarity":"use_method","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}