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setup more powerful vm for processing data & training models #mused
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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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get 108k+ runs on my public speech to text pipeline replicate.com/thomasmol/whisp…
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Finally added support for new embedding model (text-embedding-3) and the new GPT 4 Turbo (0125) for #pdfpals
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Make notebook to iteratively process full text with fine trained model #guard
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talk about LLM finetuning and Alpaca and ChatGPT being just instruct-text model #life fxtwitter.com/levelsio/status…
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try BLIP3, the same text model that ChatGPT uses to describe images and that Flux uses to generate images (I think) #photoai
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write article about how we reduced an API endpoint from 16s to 3s #blog2
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did more sweep.dev updating with the new open ai 128k context size. I wasn't seeing any improvements though
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Switch from real-time inference to Dataframe-inference
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set up stable diffusion ML text-to-image model on MacBook #life
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Switched all low-fidelity AI tasks model from 3.5-turbo to the newest gpt-4o-mini. #retently
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#thecompaniesapi work on phi3 again :) continue my search for the best prompt
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update transcribe model to whisper v3 for #audiogest
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Reprocess sphinx data to have full but smaller dataset for developing #mused
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resize json from 16mb to 3mb #cardmapr
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improve #mirage SSE API so that we can stream more than LLM output chunks and provide JSON data for new secret #crisp product
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Test batching inference #lyricallabs
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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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