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#thecompaniesapi big LLM results from our pipeline now gets saved as json files to prepare datasets for our own models ; optimizing the queue does not seem to be a finite task
#thecompaniesapi early LLM results are bonkers but the qop/s are relatively slow; setting up results storage to train smaller models; one step at a time
got actually working dataset for clv #ml4all
sent potential customer dataset #sponsorgap
provision batch of L4 and L40S GPUs at Scaleway for #mirage since our account got validated and quotas lifted
Work on #nichewit, pull in production datasets to iterate bit faster locally
finish migrating #mirage kubernetes intel and nvidia gpu instances to scaleway, getting last-generation NVIDIA L40S + L4 GPUs, running much smoother now! (previously: old A40 and A16)
#thecompaniesapi add storage pipeline for our new extraction feature; add endpoints to our storage server to automatically build fine tuning datasets from our production extraction
render out two videos from neural networks on tiny datasets to test new method of capture & pipeline #mused
setup more powerful vm for processing data & training models #mused
Ran some local LLM tests 🤖
Updating Automatic 11, Installing a new video gpu, today its a day to learn how to train local models :B #dailywork
migrate #crisp livetranslate to the mirage translation model, running on our own gpus
Chunk dataset big files and import to DB (Data Science project) #zg