DeepSeek is revolutionizing AI with DeepSeek-R1, an advanced language model that leverages reinforcement learning (RL) to enhance reasoning. Unlike traditional models that rely on massive supervised datasets, DeepSeek-R1 learns through trial and error, significantly improving problem-solving capabilities.
๐๐๐๐ฉ๐๐๐๐ค-๐๐ ๐ฏ๐ฌ. ๐๐-๐๐๐ซ๐จ: ๐ ๐๐ฆ๐๐ซ๐ญ๐๐ซ ๐๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง
DeepSeekโs research introduced two variations:
- DeepSeek-R1-Zero: The first experiment with pure RLโachieving 71% accuracy on AIME 2024 but with readability challenges.
- DeepSeek-R1: A more refined model that blends supervised learning + RL, enhancing both accuracy and fluency while maintaining 671B parameters.
๐๐ซ๐๐๐ค๐ข๐ง๐ ๐๐จ๐ฐ๐ง ๐ญ๐ก๐ ๐๐ซ๐๐ข๐ง๐ข๐ง๐ ๐๐ซ๐จ๐๐๐ฌ๐ฌ
- DeepSeek-R1-Zero: Simple RL training with reward-based learning.
- DeepSeek-R1: A four-stage training process including supervised fine-tuning, reinforcement learning, data refinement, and final optimization.
๐๐ก๐๐ญ ๐๐๐ค๐๐ฌ ๐๐๐๐ฉ๐๐๐๐ค-๐๐ ๐๐ญ๐๐ง๐ ๐๐ฎ๐ญ?
๐ Superior Performance: Scores 79.8% on AIME 2024, outperforming OpenAIโs o1โ1217 at 79.2%.
๐ฐ Cost-Effective: API pricing at $0.14 per million tokens, making it more affordable than competitors.
๐ ๏ธ Versatile Deployment: Available via DeepSeek Chat, API, or local setup with various model sizes.
๐๐จ๐ฐ ๐ญ๐จ ๐๐๐ฉ๐ฅ๐จ๐ฒ ๐๐๐๐ฉ๐๐๐๐ค-๐๐?
- Web Access: Log in to DeepSeek Chat and activate โDeep Thinkโ mode for step-by-step reasoning.
- API Integration: Compatible with OpenAIโs format, allowing seamless integration into applications.
- Local Deployment: Supports GGML, GPTQ, HF formats for optimized local performance.
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๐ป Using Ollama: Run the model locally with a single command: ollama run deepseek-r1:8b
For larger models, use 70b instead of 8b.
๐๐๐ซ๐๐ฐ๐๐ซ๐ ๐๐๐ช๐ฎ๐ข๐ซ๐๐ฆ๐๐ง๐ญ๐ฌ
- Full Models: Require a high-end GPU (RTX 3090 or better) and 48GB RAM.
- Distilled Models: Smaller models (1.5B-70B) can run on 6GB VRAM GPUs or 4GB RAM CPUs.
๐๐๐ฒ๐จ๐ง๐ ๐๐๐ฑ๐ญ: ๐
๐ฎ๐ญ๐ฎ๐ซ๐ ๐๐ฆ๐ฉ๐ซ๐จ๐ฏ๐๐ฆ๐๐ง๐ญ๐ฌ & ๐๐ฌ๐ ๐๐๐ฌ๐๐ฌ
DeepSeekโs roadmap includes:
- Enhanced software engineering task performance.
- Better multilingual handling for diverse applications.
- Improvements in role-based AI interactions & function calling.
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