Greg Isenberg - DeepSeek R1 - Everything you need to know
Ray Fernando, a former Apple engineer, discusses the use of advanced AI reasoning models, specifically Deep Seek R1, which is open-source and comparable to ChatGPT's models. These models offer superhuman reasoning capabilities and are free to use on Deep Seek's website. However, users should be cautious about data privacy, especially when data is sent to servers in China. Alternatives include using local setups or other API providers like Fireworks or Gro, which host models outside China. Ray demonstrates how to run these models locally using Docker and Open Web UI, allowing users to maintain data privacy and control. He also highlights the potential of these models in various fields, such as legal and medical, and emphasizes the importance of experimenting with different models and settings to find the best fit for specific tasks.
Key Points:
- Deep Seek R1 is an advanced AI reasoning model, open-source and comparable to ChatGPT's models, offering superhuman capabilities.
- Users should be cautious about data privacy when using Deep Seek's website, as data is sent to China.
- Alternatives include using local setups or API providers like Fireworks or Gro to maintain data privacy.
- Running models locally with Docker and Open Web UI allows for data control and privacy.
- Experimenting with different models and settings is crucial to finding the best fit for specific tasks.
Details:
1. 🚀 Introduction to Ray Fernando
- Ray Fernando, a former Apple engineer with 12 years of experience, is actively involved in AI coding and is building an AI startup.
- The discussion focuses on 'prompting with new reasoning models', highlighting the capabilities and applications of Deep Seek R1.
- Deep Seek R1 is an open-source model from China, offering reasoning capabilities similar to Chat GPT's 01 model, but with superhuman reasoning capabilities.
- The model is accessible for free on their website, promoting wide accessibility and use.
- The discussion includes the architecture of Deep Seek and provides methods for running it in different regions, ensuring data privacy and compliance.
- Insights are shared on the importance and benefits of running the model locally for private businesses, which boosts privacy and security for professionals across various sectors.
2. 🧠 Unpacking Deep Seek and AI Reasoning Models
2.1. Accessing Deep Seek and Data Concerns
2.2. Local Machine Usage and Flexibility
2.3. Video Transcription and Model Integration
2.4. Using Deep Seek with Prompts
3. 🌍 Navigating Data Privacy with AI Models
3.1. Data Privacy and Hosting Locations
3.2. Startup Empire Membership Benefits
3.3. AI Model Parameters and Performance
4. 🔧 Leveraging Open Web UI for Local AI Hosting
- Open Web UI provides a ChatGPT-like interface that simplifies local AI hosting, allowing users to manage their models directly on their own servers.
- This approach mitigates reliability issues commonly faced with third-party AI services by maintaining control over the hosting environment.
- Users can connect to API providers such as Fireworks AI to access specific AI models, with the Deep Seek model being one example.
- Implementing local AI hosting with Open Web UI ensures data privacy, as data does not need to be sent to external servers, reducing exposure to privacy risks.
- API keys are used to secure access to models, which adds a layer of protection to user data.
- A common challenge encountered is server busy errors, which necessitate retry mechanisms to ensure data is successfully processed by the AI service.
5. 💸 Balancing Model Performance and Cost
- The Gro API and distilled llama 70b model offer fast responses ideal for quick analysis, balancing speed and detail with concise outputs akin to small blog posts.
- Full models provide more detailed analyses but are slower, indicating a trade-off between speed and depth of insight.
- Reasoning models like Deep Seek and 01 Pro focus on detailed instruction adherence, adding value despite higher operational costs, such as an additional $200 monthly for OpenAI services.
- Hosting large models (600+ billion parameters) requires substantial GPU resources and reliable providers like Fireworks and Grock, showing the need for strategic hosting decisions.
- Strategic selection of hosting providers is crucial to ensure data remains within preferred regions, avoiding transfers to regions like China.
- These models revolutionize content creation with outputs comparable to human-written reports, customizable to include graphs and other formats, enhancing strategic content delivery.
6. 🛠️ Step-by-Step Local Model Setup
6.1. Pricing Strategies
6.2. Future Model Developments
6.3. Prompt Optimization Techniques
6.4. Information Verification Methods
7. 📱 Exploring Mobile AI Capabilities
7.1. Setting Up Open Web UI
7.2. Running Model Locally
7.3. Downloading and Configuring Models
7.4. Model Execution and Temperature Settings
7.5. User Interface and Testing Different Models
7.6. Running and Testing Models Locally
7.7. Advanced Configuration and API Integration
8. 📈 AI Applications: Present and Future Potential
- Apollo app facilitates downloading AI models directly to mobile devices, providing a private and local AI experience, enhancing user privacy.
- The app integrates various AI providers like open router, offering access to multiple models with some available for free through credits, showcasing a diverse AI ecosystem.
- Local model functionality is contingent on device memory capacity, with some models requiring up to 4GB, underlining the importance of adequate device storage.
- Examples of available models include distilled llama 8bit mlx and distilled Quin version at 7B, highlighting options tailored to different device specifications.
- Running AI locally offers offline capabilities, crucial for privacy and accessibility without internet reliance, promoting user autonomy.
- Future developments could see AI running on wearable tech like smartwatches, providing real-time assistance, crucial during emergencies.
- Apple's optimization for AI models ensures efficient performance on smaller devices, leveraging Apple's mlx infrastructure for improved processing efficiency.
9. 🤝 Final Thoughts and Encouragement for AI Exploration
- GPT-4 and ChatGPT's Omni models can analyze audio and tone, useful for negotiation by identifying differences in tone, cadence, and analyzing micro-expressions for enhanced decision-making.
- Running AI models locally, such as on a Mac using web UI with Docker, offers privacy and security by keeping personal data out of external servers, encouraging safe exploration of AI capabilities.
- The use of mobile platforms like Apollo app allows practical AI applications on phones without compromising data privacy, demonstrating flexibility and accessibility in AI technology.
- Exploring AI through platforms like an AI playground provides opportunities to generate novel prompts and applications, highlighting the potential for creative and valuable outcomes.
- Emphasizing data privacy, the advice is to avoid platforms that might compromise personal information, and instead use secure and transparent systems.
- Engaging with community platforms to share and develop innovative ideas fosters collaborative learning and discovery, enhancing overall understanding and application of AI.