🔊 FineTune : un mélangeur de volume open source pour macOS

👉 https://www.justgeek.fr/finetune-controle-volume-application-macos-145992/

#FineTune #Application #OpenSource #macOS #Mac #Mélangeur #Audio #Volume #Son

Der Strom an spannenden OpenSource-Apps reißt nicht ab ... viele tolle Tipps dank Caschys Blog ... heute: #FineTune

https://stadt-bremerhaven.de/finetune-open-source-lautstaerkemixer-fuer-macos-regelt-apps-separat/

#OpenSource #FOSS #FLOSS #macOS

Bạn đang muốn huấn luyện mô hình cục bộ cho ngôn ngữ lập trình tùy chỉnh (giống AutoHotKey/Lua) trên PC RTX 5070 Ti? Các bạn chia sẻ workflow, siêu tham số (Rank/Alpha) để giữ kiến thức chung và tránh hallucination khi dataset nhỏ. #AI #MachineLearning #Finetune #Coding #LLM #AIVietnam

https://www.reddit.com/r/LocalLLaMA/comments/1qhdqkh/which_model_to_finetune_on_a_new_coding_language/

FineTune – Mixer âm thanh mã nguồn mở cho macOS, giúp điều chỉnh âm lượng theo từng ứng dụng & định tuyến âm thanh độc lập. Không cần driver, không restart. Dùng miễn phí, viết bằng SwiftUI, tích hợp thanh menu. Phù hợp ai cần phát nhạc trên loa ngoài trong khi giữ âm thanh trình duyệt ở tai nghe/MacBook. Đóng góp & báo lỗi trên GitHub! #FineTune #macOS #OpenSource #ÂmThanh #CôngCụ #Tool #Music #MacOSApp #ÂmLượng #VolumeControl #SideProject

https://www.reddit.com/r/SideProject/comments/1qgs32z/

Create Custom AI Characters Easily 🎭 How To Fine-Tune LLMs For AI Role Play

https://videos.viorsan.com/w/1QZBFyJJzmN2Cyqxp1XM5e

Instagram Will Start Letting You Pick What Shows Up in Your Reels

https://fed.brid.gy/r/https://www.wired.com/story/instagram-lets-you-pick-what-shows-up-in-reels/

Should you #finetune or should you wait?

Finetuning is still an option to improve the quality of the results, but a costly one (in terms of data, time and expertise).

So if you can #wait, do it. It is likely the next generation of #LLMs will be good enough for you.

This is what we tested in a recent work where we show that current LLMs beat fine tuned versions of older ones in the education domain, in particular in question generation tasks.

And we've seen this also in other domains where the top finetuned models on a given task get obsolete by newer general #LLMs.

AI question. Say I #finetune #llama2 by #finetuning on a text task:

deciding if a new text contains new information over the original

and

label the training sets with: new-information (A) , and no-new-information (not-A)

how does the #llm reliably interpret the question:

Is there new information in this text as compared with the original as uploaded?

How does a llm make the link to the label given in the training? After all: the labels names could have been just A and not-A ?