데브허브 | DEVHUB | DeepSeek R1 REVOLUTIONIZES Open Source AI (Beats O1!)
🔬 DeepSeek R1: Breakthrough in Open Source AI
A comprehensive look at DeepSeek R1, the new open-source model matching OpenAI's performance!
🎯 Key Highlights:
Full open source model with MIT license - free for commercial use
Performance matching OpenAI's latest models on key benchmarks
Significantly lower costs: Only $0.55/1M input tokens & $2/1M output tokens
Available via API and chat interface
Excels in math, coding, and reasoning tasks
💡 Model Details:
Two versions available: DeepSeek R1-0 (base) and DeepSeek R1 (fine-tuned)
Uses reinforcement learning in post-training
Minimal labeled data required
Superior performance on AIME 2024, Math 500, and SWE Bench Verified
🔧 Technical Features:
Self-verification capabilities
Advanced reflection mechanisms
Long chain-of-thought processing
Complete development pipeline available on GitHub
🚀 Access Options:
Run locally using VLLM serve
32B parameter model coming to Ollama
Free chat interface available
API access with flexible pricing
✨ Performance Tests Demonstrated:
Python: Regular Expression Matching
JavaScript: Fiscal Code Challenge
C#: Three Sum Problem
Advanced Logical Reasoning Tests
Complex Mathematical Problem Solving
Timestamp:
0:00 - Introduction to DeepSeek R1 Model
1:04 - Cost Comparison with Other Models
1:36 - DeepSeek R10 vs R1 Model Differences
2:14 - Distilled Model Versions
2:38 - Local Testing & Performance
3:21 - Programming Challenge Tests
4:26 - Logical Reasoning Tests
5:19 - Final Thoughts
#AI #MachineLearning #DeepLearning #OpenSource #Programming #ArtificialIntelligence #Technology #Coding #OpenAI #deepseek
📝 Note: All test results and comparisons mentioned in this video are based on our independent testing. Results may vary depending on specific use cases and implementations.
Feel free to drop your questions and experiences with DeepSeek R1 in the comments below! 👇
![[취업을 위한 CS 지식] 22강. 배열과 연결 리스트](https://i2.ytimg.com/vi/QwbdNN5UnGY/hqdefault.jpg)
