Thomas Thoresen @thomas_thoresen
Father, athlete, coder. Working on @vespaengine Joined May 2022-
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I love listening to audiobooks during my commute. 20mins a day ->×~25 books a year. What should be added to my queue?
After 3 weeks at @vespaengine, I still get more and more impressed every day by both the engine and the team 🤩 Check out this blog post to explore some features that sets it apart!
After 3 weeks at @vespaengine, I still get more and more impressed every day by both the engine and the team 🤩 Check out this blog post to explore some features that sets it apart!
The latest Vespa newsletter is here to help you stay up to date on what's happening on the leading edge in RAG, IR and vector search: - A new SPLADE embedder - ONNX models with float16 - @cohere embedding model guides - Support for an array of chunks with ColBERT - And list of…
"For a production ready vector DB, I recommend Vespa" 💪 @vespaengine
"For a production ready vector DB, I recommend Vespa" 💪 @vespaengine
Seems everybody is migrating their search and recommendation systems from Elastic to Vespa now. Here's the experience of Stanby, Japan's leading job search site: blog.vespa.ai/migrating-to-t…
It is so cool that @Stanby_inc , a leading job search service in Japan, has migrated to using @vespaengine : "Vespa was chosen as the unified search engine because it aligns well with the characteristics of Stanby’s search requirements": blog.vespa.ai/migrating-to-t…. Thanks to…
A review in Nature, by @candice_odgers, asserts that I have mistaken correlation for causation and that “there is no evidence that using these platforms is rewiring children’s brains or driving an epidemic of mental illness.” Both of these assertions are untrue.…
Great to see an open source model from these guys. Remember playing with their Jurassic model (178B) in fall 2021. Can imagine they have learned a lot since then.
Great to see an open source model from these guys. Remember playing with their Jurassic model (178B) in fall 2021. Can imagine they have learned a lot since then.
Apparently this week is binarized embedding week! Fits perfect in the most versatile vector database: @vespaengine We did deep dives into BPR (binary passage retriever) in the early days, now it’s clear that this is the right direction blog.vespa.ai/billion-scale-…
Apparently this week is binarized embedding week! Fits perfect in the most versatile vector database: @vespaengine We did deep dives into BPR (binary passage retriever) in the early days, now it’s clear that this is the right direction blog.vespa.ai/billion-scale-…
This is an example of a phased coarse-to-fine ranking pipeline. This you can only do with @vespaengine. Nils also touches on the limitations of default provisioning settings of EBS volumes. An insightful thread on both performance and storage-tier economics for vector search.
This is an example of a phased coarse-to-fine ranking pipeline. This you can only do with @vespaengine. Nils also touches on the limitations of default provisioning settings of EBS volumes. An insightful thread on both performance and storage-tier economics for vector search. https://t.co/2pm3OHWJsI
Am I the only one thinking it's kinda nice that it seems possible to extract parameters from closed model API's such as ChatGPT? Only embedding layer for now, but interesting research direction for sure. (Don't disable logprobs plz @OpenAI ) arxiv.org/pdf/2403.06634…
They disrupted python linting with `ruff`. ✅ Now, they're doing package management with `uv`. 🙌 First impression: 🚀🔥
They disrupted python linting with `ruff`. ✅ Now, they're doing package management with `uv`. 🙌 First impression: 🚀🔥
Ravindra Harige @ravo
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199 Followers 213 Following Founder / Head of Customer Success, https://t.co/LMRxGp5cwM - @vespaengine - ex YahooZeta Alpha @ZetaVector
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only in SF will the biggest guy in the gym be wearing a MongoDB t shirt
Awesome to see HQQ being put to good use. Whisper is the work horse for many ASR systems in the world, and it just got faster and cheaper. Great work @kadirnar_ai
Transcribe 1-hour videos in 20 SECONDS with Distil Whisper + Hqq(1bit)! Thanks to @Mobius_Labs @younesbelkada @huggingface ❤️ WhisperPlus: github.com/kadirnar/whisp… Hqq: github.com/mobiusml/hqq Note: Tested on RTX 3090 device.
🎉 Big news! I've joined @LiquidAI_, an MIT spin-off, where I'm leading the efforts to fine-tune LLMs. They've got big plans and serious compute power, so I'm excited to see what we can accomplish :) If you want to meet in person, I'll be at our social event at @iclr_conf in…
Hey Kilian, a research article about the bicarb system has been published. Would you like to read it? - sure! - while running in a treadmill… -ok… - in the top of a mountain… - 🤌 🍿Full video-article here : maurten.com/innovation/sci…
If you are one of the 27 people with tldraw open in a tab that hasn’t refreshed in three months, please refresh the page
This is the paper that convinced me - proceedings.mlr.press/v28/coates13.p… Showing that a Frankenstein CUDA cluster could beat a 10,000 cpu map reduce cluster
# CUDA/C++ origins of Deep Learning Fun fact many people might have heard about the ImageNet / AlexNet moment of 2012, and the deep learning revolution it started. en.wikipedia.org/wiki/AlexNet What's maybe a bit less known is that the code backing this winning submission to the…
Oh, remember Theano and Caffe? It feels too long ago now 😅
@NielsRogge Hosting your own models will also have downtimes, like any infrastructure. The question is more if you or the API provider have the better skills and tech to achieve a high availability.
My last work at Microsoft Research is finally released: github.com/microsoft/MS-M… 10 MILLION REAL Bing search queries with 60 MILLON+ REAL user clicks on 10 BILLION ClueWeb22 documents. Have fun scaling up!
Not going to lie seeing these tweets used to give me so much stress. 😆 The pace the Vespa team is able to roll these things out is next level. I've realized that there is a strong case for a vector-centric data store like @vespaengine.
One of the use cases I'm most excited about when we now have support for gguf LLM inference in Vespa is query rewriting and classification. This is an area that everybody in the industry is talking about, but there are few practical examples.
In addition, Vespa supports the fourth and maybe most important representation: sparse unlearned representations that you can use for BM25 scoring. This includes linguistics integrations for 40+ languages.
Months have passed, and @vespaengine is still the only vector database that supports all three representations of BGE-M3. All three representations in the same query and with a hybrid score combination of all three representations in ranking 🚀 pyvespa.readthedocs.io/en/latest/exam…
Me when I see a search startup not using BM25:
Woah, Colbertv2 has broken into the top 100 most downloaded models on @huggingface now. @lateinteraction has gone mainstream!
Happy Friday 😅
@DynamicWebPaige Thank you! 😊 This is a much better version than what I managed to articulate, love it! 🤓❤️
The long-awaited and requested FastAPI CLI is here! 🎉 And there's sooo much more to come... 🎁😎
Here's the new FastAPI CLI! ✨🎉 Upgrade to FastAPI version 0.111.0 (just released) and you'll have it in your terminal. 😎 fastapi.tiangolo.com/fastapi-cli/