Artwork

Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

Orca 2: Enhancing Reasoning in Smaller Language Models - Technical Details

8:48
 
Share
 

Manage episode 421181730 series 3474159
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/orca-2-enhancing-reasoning-in-smaller-language-models-technical-details.
Orca 2 enhances small language models' reasoning by teaching diverse strategies for tasks, outperforming models up to 10x larger in complex benchmarks.
Check more stories related to programming at: https://hackernoon.com/c/programming. You can also check exclusive content about #language-models, #orca-2, #reasoning-techniques, #machine-learning, #small-models, #imitation-learning, #ai-benchmarks, #model-training, and more.
This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com.
The Orca 2 dataset has four main sources:FLAN: Our main source of prompts for synthetic data generation is the FLAN-v2 Collection 33, which consists of five sub-collections. Following Orca 1 42, we consider tasks from only CoT, NiV2, T0, Flan 2021 and Dialogue. Some of the tasks are associated with an associated answer. For the Cautious Reasoning dataset we selected ~602 zero-shot user queries from the split of 1448 high quality tasks out of 1913.

  continue reading

346 episodes

Artwork
iconShare
 
Manage episode 421181730 series 3474159
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/orca-2-enhancing-reasoning-in-smaller-language-models-technical-details.
Orca 2 enhances small language models' reasoning by teaching diverse strategies for tasks, outperforming models up to 10x larger in complex benchmarks.
Check more stories related to programming at: https://hackernoon.com/c/programming. You can also check exclusive content about #language-models, #orca-2, #reasoning-techniques, #machine-learning, #small-models, #imitation-learning, #ai-benchmarks, #model-training, and more.
This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com.
The Orca 2 dataset has four main sources:FLAN: Our main source of prompts for synthetic data generation is the FLAN-v2 Collection 33, which consists of five sub-collections. Following Orca 1 42, we consider tasks from only CoT, NiV2, T0, Flan 2021 and Dialogue. Some of the tasks are associated with an associated answer. For the Cautious Reasoning dataset we selected ~602 zero-shot user queries from the split of 1448 high quality tasks out of 1913.

  continue reading

346 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Quick Reference Guide

Copyright 2025 | Privacy Policy | Terms of Service | | Copyright
Listen to this show while you explore
Play