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Nina Montana Brown

NMontanaBrown
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reacted to vikhyatk's post with πŸ”₯ 3 months ago
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3269
Just released a dataset with 7000+ hours of synthetically generated lo-fi music. vikhyatk/lofi
reacted to davanstrien's post with πŸš€ 9 months ago
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Could more DPO-style preference data be crucial for enhancing open LLMs across different languages?

Leveraging a 7k preference dataset Argilla ( @alvarobartt ), Hugging Face ( @lewtun ) and Kaist AI ( @JW17 & @nlee-208 )
utilized Kaist AI's recently introduced ORPO technique ORPO: Monolithic Preference Optimization without Reference Model (2403.07691) with the latest MistralAI MOE model to create a very high-performing open LLM: HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1

Since ORPO doesn't require a separate SFT stage, all that is needed is a strong base model + high-quality DPO-style datasets.

Currently, there is a significant lack of non-English DPO datasets. Filling this gap could significantly improve open LLMs in various languages.

You can get an overview of the current state of DPO datasets across different languages here: https://huggingface.co/spaces/DIBT/preference_data_by_language
reacted to Jaward's post with πŸ”₯ 9 months ago
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On Agentic AI: Autonomy Is All You Need!

There is this remarkable beauty in witnessing an AI system autonomously completes complex tasks with a level of brilliance that supersedes our reasoning capabilities/expectations - It is the holy grail of creation.

Giving your AI agents autonomy is analogous to us having "free will" and everything else thereafter is a cascade of possibilities and potentials waiting to unfold.

As brilliantly said by @AndrewNg "It’s a beautiful thing when you see an agent autonomously decide to do things in ways that you had not anticipated, and succeed as a result!"

Autonomy in Agentic AI
- augments agents' decision-making capabilities.
- enables adaptation to diverse environments.
- facilitates real-time learning and improvement.
- fosters dynamic multi-agent collaboration.
- promotes efficient and independent task execution.
- drives innovation in dynamic and unpredictable
scenarios.

This what the AutoAgents paper conveyed - A fully autonomous agentic framework that basically gives agents the free will for authentic/compelling creativity.

With just three dynamically predefined agents [ Planner, Agent Observer and Plan Observer ] acting collaboratively they are able to generically create autonomous agents:

Agent (A) =  { 
     Prompt (P) - defines agent's identity fully , 
     Description (D) - adds specific role identity, 
     Toolset (T) - equips the agent with tools, 
     Suggestion (S) - offers task execution tips
 }


Demo: LinkSoul/AutoAgents
Code: https://github.com/Link-AGI/AutoAgents
Paper: https://arxiv.org/abs/2309.17288