SelmaNajih001/FinancialTimes_MultiCompany
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Selma Najih PRO
SelmaNajih001
AI & ML interests
Large Language Models applied to finance
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updated a dataset about 3 hours ago
SelmaNajih001/FinancialTimes_MultiCompany updated a dataset about 12 hours ago
SelmaNajih001/Cnbc_MultiCompanyOrganizations
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dataset about 3 hours ago
updated a
dataset about 12 hours ago
reacted to fffiloni's post with π₯ 12 days ago
Post
3437
I built HF Radio on Hugging Face Spaces π»
fffiloni/HF-Radio
A live community radio for AI-generated songs, powered by tracks created with ACE-Step.
You can tune in, discover community-made songs in many languages, vote on what sounds good, and mark your real favorites as Bangers.
The more people listen, vote, and create, the better the station gets.
Under the hood, it connects a few Hugging Face pieces together:
Spaces for the live app, HF buckets for community tracks, OAuth for signed-in listeners, server-side streaming with ffmpeg, hourly playlist refreshes, moderation, jingles, and community feedback loops.
Itβs not just a playlist.
Itβs a shared taste experiment:
new songs get a shot every hour, and the community helps decide what deserves another spin.
Come listen.
Find weird gems.
Support the Bangers.
Shape the radio.
β> fffiloni/HF-Radio
fffiloni/HF-Radio
A live community radio for AI-generated songs, powered by tracks created with ACE-Step.
You can tune in, discover community-made songs in many languages, vote on what sounds good, and mark your real favorites as Bangers.
The more people listen, vote, and create, the better the station gets.
Under the hood, it connects a few Hugging Face pieces together:
Spaces for the live app, HF buckets for community tracks, OAuth for signed-in listeners, server-side streaming with ffmpeg, hourly playlist refreshes, moderation, jingles, and community feedback loops.
Itβs not just a playlist.
Itβs a shared taste experiment:
new songs get a shot every hour, and the community helps decide what deserves another spin.
Come listen.
Find weird gems.
Support the Bangers.
Shape the radio.
β> fffiloni/HF-Radio
reacted to vincentg64's post with π₯ 12 days ago
Post
950
96% Correct Next Token Prediction, with No DNN, no Training, auto-distilled model - https://mltblog.com/4urfvTB
Over the last 12 months, Iβve built a model to predict the next token and to suggest synonyms or related queries to a user prompt, with 100% correct predictions on the training set in one shot, without training or deep neural networks (DNNs). The same model is now integrated in some of the most recent LLM architectures, albeit with costly training via DNNs. My version does not need DNNs or training.
The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. The new model is as a substitute to standard DNNs, with increased explainability and higher accuracy. It is designed for corporate corpuses. The end goal is to provide better accuracy at a much lower cost, while providing full control over all the components.
An interesting feature is auto-distillation, whereas the model self-identifies weights that do not contribute over time in 99.9% of user-generated prompts, and drop them, based on prompts from a large, specialized user base. The gain is most spectacular in open-weight LLMs applied to specialized contexts, whether based on DNNs or not.
Read article and download the free technical paper with NVIDIA case study, at https://mltblog.com/4urfvTB
Over the last 12 months, Iβve built a model to predict the next token and to suggest synonyms or related queries to a user prompt, with 100% correct predictions on the training set in one shot, without training or deep neural networks (DNNs). The same model is now integrated in some of the most recent LLM architectures, albeit with costly training via DNNs. My version does not need DNNs or training.
The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. The new model is as a substitute to standard DNNs, with increased explainability and higher accuracy. It is designed for corporate corpuses. The end goal is to provide better accuracy at a much lower cost, while providing full control over all the components.
An interesting feature is auto-distillation, whereas the model self-identifies weights that do not contribute over time in 99.9% of user-generated prompts, and drop them, based on prompts from a large, specialized user base. The gain is most spectacular in open-weight LLMs applied to specialized contexts, whether based on DNNs or not.
Read article and download the free technical paper with NVIDIA case study, at https://mltblog.com/4urfvTB
reacted to sergiopaniego's post with π 12 days ago
Post
9948
Harness, Scaffold, Context Engineering, Agent... do you actually know what they mean?
We wrote an AI agent glossary and tried to make sense of it all with simple definitions and real examples
β go read it β
https://huggingface.co/blog/agent-glossary
We wrote an AI agent glossary and tried to make sense of it all with simple definitions and real examples
β go read it β
https://huggingface.co/blog/agent-glossary
- 2 replies
upvoted an article 12 days ago
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Harness, Scaffold, and the AI Agent Terms Worth Getting Right
sergiopaniego, ariG23498
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The Annotated Diffusion Model
nielsr, kashif
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