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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
#thecompaniesapi lots of improvements on our robot, now track per-job metrics, also track input/output tokens for all our AI queries, started collecting dataset on sample domains for our fine-tuning
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