Click here to download the project base paper of autoregressive language models project
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Abstract:
We provide autoregressive language models project of Machine Learning-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, autoregressive language models project carry the same risks as their foundational language models. In particular,
these models train on web-scraped data, and they
have not undergone safety-focused fine-tuning.
Models thus may produce unexpected, inappropriate, or inaccurate outputs. We hope to further
investigate the safety properties of autoregressive
vision-language models like OpenFlamingo. OpenFlamingo, a family of five autoregressive visionlanguage models across the 3B, 4B, and 9B scales. OpenFlamingo remains an active research project,
and we continue to work on training and releasing high-quality autoregressive vision-language
models. We hope our contribution enables more
researchers to train and study such models. However, assuming a single image as input is
limiting: autoregressive vision-language models
enable new capabilities by instead mapping an
arbitrarily interleaved sequence of images and text to textual outputs. This interface provides
important flexibility: the input sequence can include demonstrations for a new task, enabling fewshot, in-context learning [3] or multi-round multimodal chatbot interactions. Evaluations suggest
that autoregressive vision-language models can
be performant foundation models [5]: models like
Flamingo [3], CM3 [1], Kosmos-1 [12], PALME [8], and multimodal GPT-4 [28] generalize well
across diverse vision-language tasks. Unfortunately, these autoregressive visionlanguage models are closed-source, and their
weights, training data, code, and hyperparameters are proprietary. This limits the academic
community’s ability to conduct research on autoregressive vision-language models, e.g., to understand how web-scraped image-text data affects models’ performance and safety. We share our models and code click here
