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You will have to send one page at a time, most of this work has to be done via RAG. Adding a large context (like a whole PDF), still does not work that well in my experience.

The ablation studies and the dataset can be found here: https://www.zyphra.com/post/building-zyda-2

There are some integrations for stuff like https://docs.rapids.ai/visualization : HoloViews hvPlot Datashader Plotly Bokeh Seaborn Panel PyDeck cuxfilter node RAPIDS

There is dask cudf which gets a lot of the way there. https://docs.rapids.ai/api/dask-cudf/stable/

I was conversing with it in Hinglish (A combination of Hindi and English) which folks in Urban India use and it was pretty on point apart from some use of esoteric hindi words but …

Nvidia announces Nemotron-4 15B introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates st…

Its seamless to accelerate BERTOPIC on GPU's with cuML now with the latest release. (v0.10.0) Checkout the docs at: https://maartengr.github.io/BERTopic/faq.html#can-i-use-the-... …

On the SQL front there has been some active work to make that experience better with DASK. See dask-sql: https://dask-sql.readthedocs.io/en/latest/pages/api.html

You can probably use https://github.com/rapidsai/cudf/tree/main/python/dask_cudf a dask wrapper around cuDF.

RAPIDS by NVIDIA has an equivalent API open source version of Sckit-Learn https://docs.rapids.ai/api/cuml/stable/ which seems to offer 100x speedup for a lot of these models.