PixelFlow allows you to use all these features
Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.
Segmented Creation Workflow
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customized Output
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Layering Different Models
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Workflow APIs
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
DeepSeek-R1
DeepSeek-R1 is a first-generation reasoning model developed by DeepSeek-AI, designed to excel in complex problem-solving. It builds upon the foundation of the DeepSeek-V3-Base model and incorporates advancements in reinforcement learning (RL). The model comes in several versions, including DeepSeek-R1-Zero and various distilled models.
Key Features of DeepSeek-R1
Advanced Reasoning: The model uses a unique training pipeline combining reinforcement learning and supervised fine-tuning to achieve high performance in reasoning, math, and code-related tasks.
Reinforcement Learning: DeepSeek-R1-Zero was trained using large-scale reinforcement learning without supervised fine-tuning, enabling self-verification, reflection, and long chain-of-thought reasoning.
Cold-Start Data: To address issues like repetition, readability, and language mixing in DeepSeek-R1-Zero, DeepSeek-R1 incorporates cold-start data prior to RL training.
Distillation: The reasoning capabilities have been successfully transferred into smaller models while maintaining high performance.
Open Source: The base models and six dense distilled models based on Llama and Qwen are open-sourced for research.
Performance: DeepSeek-R1 achieves performance comparable to OpenAI's models across various benchmarks, with some distilled models outperforming OpenAI-o1-mini.
Parameters: 671B total with 37B activated parameters
Context Length: 128K
Performance Highlights of DeepSeek-R1
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Outperforms several models in English, code, math, and Chinese benchmarks
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Achieves top scores in MMLU-Redux, DROP, AlpacaEval2.0, ArenaHard, Codeforces, and AIME 2024
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DeepSeek-R1-Distill-Qwen-32B sets new state-of-the-art results for dense models.
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