LLMs-from-scratch
OutRun
LLMs-from-scratch | OutRun | |
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9 | 7 | |
16,129 | 689 | |
- | - | |
9.6 | 0.0 | |
2 days ago | 27 days ago | |
Jupyter Notebook | Swift | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
LLMs-from-scratch
- Finetune a GPT Model for Spam Detection on Your Laptop in Just 5 Minutes
- Insights from Finetuning LLMs for Classification Tasks
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Ask HN: Textbook Regarding LLMs
https://www.manning.com/books/build-a-large-language-model-f...
- Comparing 5 ways to implement Multihead Attention in PyTorch
- FLaNK Stack 29 Jan 2024
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Implementing a ChatGPT-like LLM from scratch, step by step
The attention mechanism we implement in this book* is specific to LLMs in terms of the text inputs, but it's fundamentally the same attention mechanism that is used in vision transformers. The only difference is that in LLMs, you turn text into tokens, and convert these tokens into vector embeddings that go into an LLM. In vision transformers, instead of regarding images as tokens, you use an image patch as a token and turn those into vector embeddings (a bit hard to explain without visuals here). In both text or vision context, it's the same attention mechanism, and it both cases it receives vector embeddings.
(*Chapter 3, already submitted last week and should be online in the MEAP soon, in the meantime the code along with the notes is also available here: https://github.com/rasbt/LLMs-from-scratch/blob/main/ch03/01...)
OutRun
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Show HN: I make an iOS app for runners using Apple Watch
Ive had a similar problem to you, but I have mostly solved it by using the Outrun app (which is free and open source), and going into the settings and turning up the GPS smoothing. I mostly run in central london, and it seems to work with the tall buildings here.
https://github.com/timfraedrich/OutRun
- FLaNK Stack 29 Jan 2024
- FLaNK Stack Weekly 22 January 2024
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OutRun – Open-source, privacy oriented, outdoor fitness tracker
https://github.com/timfraedrich/OutRun/issues/91
> I wouldn't necessarily say abandoned. I still work on it from time to time, but progress is very very slow and I cannot prioritise it over other things atm
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Oxford to Stratford Upon Avon
Shout out to the app "Out-Run" that I use to track my rides. Privacy focused way to track your rides without all of the bullshit social media that comes with Strava. No account, all data stored locally, can easily export your rides, no data collected. Have exchanged a few emails with the dev and he's a super cool dude, so I shout out his app at every opportunity. All open source as well.
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Are there any privacy-focused fitness/health tracking apps?
This is the GitHub page.
What are some alternatives?
s4 - Structured state space sequence models
RxSwift - Reactive Programming in Swift
open-source-ios-apps - :iphone: Collaborative List of Open-Source iOS Apps
Material - A UI/UX framework for creating beautiful applications.
Kingfisher - A lightweight, pure-Swift library for downloading and caching images from the web.
Hero - Elegant transition library for iOS & tvOS
awesome-swift - A collaborative list of awesome Swift libraries and resources. Feel free to contribute!
Eureka - Elegant iOS form builder in Swift
openremote - 100% open-source IoT Platform - Integrate your devices, create rules, and analyse and visualise your data
speechbrain - A PyTorch-based Speech Toolkit
GoldenCheetah - Performance Software for Cyclists, Runners, Triathletes and Coaches
ActivityLog2 - Analyze data from swim, bike and run activities