sae-rad/cxr_resized_past_reports
Viewer
•
Updated
•
3.4k
•
63
Nina Montana Brown
NMontanaBrown
AI & ML interests
None yet
Recent Activity
updated
a dataset
8 days ago
sae-rad/cxr_resized_past_reports
published
a dataset
8 days ago
sae-rad/cxr_resized_past_reports
liked
a model
2 months ago
lion-ai/MedImageInsights
Organizations
NMontanaBrown's activity
updated
a
dataset
8 days ago
published
a
dataset
8 days ago
liked
a
model
2 months ago
reacted to
vikhyatk's
post with 🔥
3 months ago
Post
3480
Just released a dataset with 7000+ hours of synthetically generated lo-fi music.
vikhyatk/lofi
liked
a
model
4 months ago
updated
a
dataset
4 months ago
updated
4
datasets
7 months ago
reacted to
davanstrien's
post with 🚀
10 months ago
Post
2032
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
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 🔥
10 months ago
Post
2279
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:
Demo: LinkSoul/AutoAgents
Code: https://github.com/Link-AGI/AutoAgents
Paper: https://arxiv.org/abs/2309.17288
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
liked
a
dataset
10 months ago
liked
a
Space
11 months ago
liked
2
models
about 1 year ago
