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In this episode, Nathan sits down with Jonathan Frankle, Chief Scientist, and Abhi Venigalla, Research Scientist of MosaicML.
In this episode, Nathan sits down with Jonathan Frankle, Chief Scientist, and Abhi Venigalla, Research Scientist of MosaicML. They chat about Mosaic’s custom LLMs, the customers seeking Mosaic out and what their journeys and use cases look like, and exciting developments in Mosaic’s research: including their new inference platform, as well as Mosaic’s MPT-7B-65k+ storywriter model.
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TIMESTAMPS:
(00:00) Episode Preview
(06:04) Mosaic’s business model
(07:28) Who uses Mosaic’s custom LLMs? What does their data look like?
(09:55)Mosaic’s use cases for custom LLMs
(12:47) How much extraction and summarization was done by humans pre-LLMs?
(15:28) Sponsor: Omneky
(21:50) The journeys of Mosaic’s customers and would a Wendy’s LLM know about a Big Mac?
(25:46) The curriculum model and fine-tuning
(29:10) Language models in the life sciences
(33:20) How raw can data be before it becomes a problem?
(35:44) Using the output of bulk pre-training process vs additional after training
(38:30) Redteaming as a service
(39:40) Mosaic’s inference platform
(41:53) Spending one cent on 20,000 tokens, how is that cent distributed?
(46:00)) Selling compute on a dedicated capacity basis
(47:30) Oracle and AWS
(49:50) The storywriter model and 65,000 token window
(54:35) The transition from finite parameters into infinite attention matrix
LINKS:
MosaicML: https://www.mosaicml.com/
MPT-7B Storywriter Model: https://huggingface.co/mosaicml/mpt-7b-storywriter
TWITTER:
@jefrankle (Jonathan)
@abhi_venigalla (Abhi)
@MosaicML (Mosaic)
@CogRev_Podcast
@labenz (Nathan)
@eriktorenberg (Erik)
SPONSOR:
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Music Credit: MusicLM
