DeepSeek-R1: Revolutionizing AI Reasoning | Try Locally using Ollama and LM Studio - Video Insight
DeepSeek-R1: Revolutionizing AI Reasoning | Try Locally using Ollama and LM Studio - Video Insight
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Deep Seek R1 boasts competitive performance and affordability compared to OpenAI's models, while also being open source and beneficial for the AI community.

The video discusses the performance of Deep Seek R1, a new generation reasoning model that has shown to perform better than OpenAI's model on various benchmarks including math, coding, and reasoning tasks. Deep Seek R1 is open source and MIT licensed, allowing the community to leverage its capabilities easily. It includes six distilled models with varying sizes, notably achieving a remarkable performance in AIMM 2024 benchmarks while maintaining significantly lower input and output costs per million tokens compared to OpenAI's offerings. Despite some limitations, such as the model's capabilities on software engineering benchmarks and available languages, Deep Seek R1 represents a significant milestone in the pursuit of effective open-source AI solutions and is poised to energize the AI community. The video illustrates practical applications of Deep Seek R1 by testing its functionalities in real time across several platforms.


Content rate: B

The content is informative and well-structured, presenting evidence and analyses of performance metrics, pricing, and limitations of the Deep Seek R1 model. While some claims require additional validation, the overall delivery of information strikes a good balance between opinion and factual reporting, making it a valuable resource for those interested in AI advancements.

AI OpenSource Benchmarking Performance

Claims:

Claim: Deep Seek R1 has limitations on multi-turn interactions and supports only English and Chinese.

Evidence: The video explicitly notes limitations in functions like complex role-playing and interaction in languages beyond English and Chinese.

Counter evidence: Many AI models initially face limitations upon release, and future updates may expand language support and functionalities, potentially countering this claim.

Claim rating: 7 / 10

Model version: 0.25 ,chatGPT:gpt-4o-mini-2024-07-18

### Key Facts about Deep Seek R1 1. **Performance Benchmarking**: - Deep Seek R1 surpasses OpenAI's models in several tests: - AIMIM 2024: 79.8% (beats OpenAI 01) - Comparable performance in Codeforces, MML, and SWE Bench. - Outperformed OpenAI in Math 500. 2. **Model Distillation**: - Deep Seek R1 distills smaller models into six versions (1.5B, 7B, 14B, 32B, 8B Llama, 70B). - Llama 70B distilled model performed exceptionally well across benchmarks, even surpassing GPT-4. 3. **Open Source and Licensing**: - Deep Seek R1 is fully open-source and MIT licensed, allowing community access and use of model weights. 4. **Cost Efficiency**: - Input cost: $0.14 per million tokens compared to OpenAI’s $7.50. - Output cost: $2.10 per million tokens versus OpenAI’s $60. 5. **Technical Capabilities**: - Large-scale reinforcement learning (RL) fine-tuning with minimal labeled data. - Effective in math, coding, and reasoning tasks. 6. **Model Accessibility**: - Users can download and utilize the models swiftly via platforms like chat.ds.com and LM Studio. 7. **Limitations**: - Cannot perform function calling, multi-turn conversations, or complex role-playing. - Currently available only in English and Chinese. 8. **Conclusion**: - Deep Seek R1 represents a significant advancement in open-source AI, pushing the boundaries of what's achievable against existing models, while also increasing accessibility and reducing operational costs.