Provider: DeepSeek AI
License: MIT
Access: Open-source (weights available)
Context Length: 128K tokens
Architecture: 671B Mixture-of-Experts (MoE) with 37B active parameters
Training: Reinforcement Learning with Human Feedback (RLHF) and supervised fine-tuning
Distilled Versions: Available in 1.5B to 70B parameters based on LLaMA and Qwen architectures
🔍 Overview
DeepSeek-R1 is a cutting-edge open-source language model developed by DeepSeek AI, designed to excel in complex reasoning tasks. Building upon its predecessor, DeepSeek-R1-Zero—which was trained purely via reinforcement learning—DeepSeek-R1 incorporates a small amount of high-quality supervised data before RL to enhance coherence and usability :contentReference[oaicite:1]{index=1}.
Key features include:
- Advanced Reasoning Capabilities: Demonstrates strong performance in mathematics, coding, and logical reasoning tasks.
- Efficient Architecture: Utilizes a Mixture-of-Experts design with 671 billion parameters, activating only 37 billion per inference, optimizing computational efficiency.
- Distillation: Offers distilled versions ranging from 1.5B to 70B parameters, maintaining robust reasoning abilities in smaller, more accessible models :contentReference[oaicite:2]{index=2}.
đź“Š Performance Benchmarks
DeepSeek-R1 achieves performance comparable to OpenAI’s o1 model across various benchmarks in math, code, and reasoning tasks. The distilled models, such as DeepSeek-R1-Distill-Qwen-32B, have set new state-of-the-art results for dense models in several evaluations :contentReference[oaicite:3]{index=3}.
🚀 Deployment
- Hugging Face Repository: deepseek-ai/DeepSeek-R1
- Inference: Compatible with standard transformer-based inference pipelines.
- Distilled Models: Available for various sizes, facilitating deployment in resource-constrained environments.