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we're turning University of Waterloo into a supercomputer! arceus is a cross-device distributed compute network for training large models, using model/tensor/pipeline parallelism. you can train anything, from deep neural networks to language models on the network. oss & deployment soon!

35,720 次观看 • 1 年前 •via X (Twitter)

11 条评论

rajan agarwal 的头像
rajan agarwal1 年前

it was a lot of fun working with @ishaandey_, @josh1yan and @simerusm on this project :) Technical Case Study: Design Case Study:

rajan agarwal 的头像
rajan agarwal1 年前

we are thankful for the opportunity to demo to & receive feedback from engineers at @openai @anthropic @telsa @apple, VCs at @a16z @sequoia @ThriveCapital @CommaCapital & C-suite at @tryramp @ollama!

UserInterface 的头像
UserInterface7 年前

The UIX Network is Live! Business & Social Networking 2.0. All the tools you need to get your $ right. By Us For Us. -It's for the people. #getoncode pls rt

andrew 的头像
andrew1 年前

@UWaterloo bro i was lied to i thought waterloo was already a super computer???

rajan agarwal 的头像
rajan agarwal1 年前

@UWaterloo LMAO

parshant 的头像
parshant1 年前

@UWaterloo looks really cool

Mayank Jain 的头像
Mayank Jain1 年前

@UWaterloo The design is so clean

Mike Bird (Hiring) 的头像
Mike Bird (Hiring)1 年前

@UWaterloo Man, when I was there, all we had was dc++

Jibraan ☾⁺ 的头像
Jibraan ☾⁺1 年前

@UWaterloo the waterloo fomo is very real

Roshan K 的头像
Roshan K1 年前

@UWaterloo that forward pass / backprop animation is sick

Wenitte Apiou 的头像
Wenitte Apiou1 年前

@UWaterloo Here for the Pokemon reference

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Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 次观看 • 2 年前