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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
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start first trainigng with Flux on GPU again now with bigger HDD #photoai
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install LM Studio and get TheBloke's latest LLMs to try new 40x GPU cores #life
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#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
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got actually working dataset for clv #ml4all
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sent potential customer dataset #sponsorgap
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provision batch of L4 and L40S GPUs at Scaleway for #mirage since our account got validated and quotas lifted
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set up my own GPU server to try #avatarai
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Work on #nichewit, pull in production datasets to iterate bit faster locally
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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)
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#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
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deploy 24GB RTX 4090 for Flux training #photoai
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render out two videos from neural networks on tiny datasets to test new method of capture & pipeline #mused
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setup more powerful vm for processing data & training models #mused
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Ran some local LLM tests 🤖
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Updating Automatic 11, Installing a new video gpu, today its a day to learn how to train local models :B #dailywork
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migrate #crisp livetranslate to the mirage translation model, running on our own gpus
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Learn about AWS Sagemaker and spin up a beastly GPU instance to do deep learning training #guard
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Chunk dataset big files and import to DB (Data Science project) #zg
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