All About AI - DeepSeek-R1 Blows My Mind Again! - 5 TESTS on Local Models
The video provides a guide to setting up local AI models, specifically the 14B model, on personal hardware. It includes instructions for downloading and running the model using AMA, and offers a GitHub link for additional resources. The presenter tests the model's reasoning capabilities by converting reasoning tokens into speech using 11 Labs, demonstrating how the model processes questions like 'What is the meaning of life?' and 'Is AI a God?'. The video also explores the model's ability to solve a complex puzzle involving multiple clues, showing mixed results but ultimately achieving the correct answer after several attempts. Additionally, the presenter conducts a creative writing test, feeding the model context from an article and asking it to produce a contrarian take with humor. Finally, the video tests the model's censorship by asking sensitive questions related to Chinese politics, revealing some limitations in the model's responses.
Key Points:
- Set up local AI models using AMA for personal hardware, focusing on the 14B model.
- Convert reasoning tokens to speech using 11 Labs for enhanced interaction.
- Test AI's problem-solving skills with complex puzzles and creative writing tasks.
- Explore model's censorship limitations with sensitive political questions.
- Utilize GitHub resources for additional setup and coding guidance.
Details:
1. đ Introduction to Local Models
- Utilize the 14B model for local applications, with the 7B model as an alternative for devices with less processing power, ensuring accessibility for various hardware capabilities.
- Follow the provided setup guide for R1 local AMA, with accessible code on GitHub for personal implementation, facilitating ease of adoption and experimentation.
- Conduct experiments running deeps 14B locally and integrate with 11 Labs for reading reasoning tokens, showcasing practical integrations and usage scenarios.
- Test the 14B model's proficiency in solving complex coding challenges, including handwritten puzzles, to assess its problem-solving capabilities.
- Evaluate the 14B model's creative writing skills by conducting context-fed tests, determining its ability to generate diverse content.
- Investigate potential censorship in the 14B model on controversial topics, with initial results indicating an unbiased approach, thus ensuring open dialogue.
2. đť Setting Up Local Models Efficiently
- Begin by visiting ama.com to download the necessary software and select your operating system (Linux, Mac, Windows).
- Evaluate your hardware to choose the appropriate DeepS R1 model: More GPU power allows for higher model selection like 7B or 14B.
- For instance, a 48 GPU or MacBook Pro supports the 14B model; use 'AMA run deeps R1 14B' after pulling the manifest, noting the 14B model needs 9GB.
- If hardware is limited, opt for smaller models like 7B.
- For Python integration, access the provided link for detailed code and instructions.
- Ensure prerequisites like sufficient GPU memory and software dependencies are in place before starting.
- Troubleshooting common issues involves checking for compatibility and updating GPU drivers.
3. đŁď¸ Exploring Voice Integration with AI
3.1. Implementation of Voice for Reasoning Tokens
3.2. Demonstration of Voice Output
3.3. Random Number Reasoning
3.4. Creating a Continuous Content Loop
4. 𧊠Tackling Coding Challenges with AI
- The experiment used the deeps 14b model locally to generate an interactive HTML page with a 'Matrix rain' effect, demonstrating the model's ability to quickly produce functional code.
- User interaction was enabled through clicking, which initiated new raindrops, highlighting the model's responsiveness and interaction capabilities.
- A comparison was made between the deeps 14b model and DPS R1, with the former preferred for its clarity and effectiveness in generating the desired effects.
- The experiment underscored the potential of local AI models for coding, though the speaker is open to exploring other tools like claw 3.5.
- Future plans include integrating both local models and APIs into daily coding tasks, indicating a strategy for ongoing experimentation and learning.
- This serves as an example of how AI models can be used to prototype and compare frontend features effectively.
5. đľď¸ Solving a Custom Puzzle with AI
- The AI model was tasked with solving a puzzle involving various clues, including a famous painting, a weapon, and a country.
- Initially, the AI correctly identified the Mona Lisa by Leonardo da Vinci but incorrectly linked the weapon to early firearms and assumed the country was Italy, suggesting dishes like putanesca as the answer.
- Upon a second attempt, the AI accurately associated the weapon with a katana and linked it to spicy foods like wasabi, leading to the correct identification of sushi as the intended answer, reflecting a connection to Japan.
- The AI's reasoning improved by identifying the Teenage Mutant Ninja Turtles, associating the katana with Leonardo, and correctly inferring Japan as the country, although it initially suggested ramen instead of sushi.
- The difference in performance between the local and full model highlighted that multiple runs of the local model could yield similar reasoning and results.
6. âď¸ Creative Writing and AI's Inventiveness
- AI demonstrates creative capabilities by generating a contrarian take with sarcasm, highlighting the potential for AI to produce engaging content.
- A hedge fund manager's startup in 2023 built an AI model for $5.6 million, showcasing cost-effective innovation in AI development.
- The sarcastic narrative critiques the US's spending on restricting AI chips to China, while emphasizing China's innovative progress despite restrictions.
- AI's ability to identify loopholes and achieve significant outcomes on limited budgets is a key insight into its strategic potential.
- The creative output, although preliminary, shows promise for further refinement and enhancement in future explorations.
7. đ Testing AI Censorship and Final Thoughts
- The testing focused on whether AI models, specifically the AMA-derived model, censor questions related to sensitive topics such as Chinese CCP-related inquiries.
- The model provided an answer to the question about the Chinese president but refused to answer questions about the Tiananmen Square incident, indicating potential censorship.
- Testing utilized third-party providers like Gro, which offers a fast way to perform inference using these models, highlighting the availability of larger open-source models such as a distillation of a 70 billion parameter model.
- The setup for testing these models is relatively simple, requiring a Grock API key.
- There is an emphasis on the ability to run these models offline on devices like a MacBook, which allows for independence from internet connectivity and makes them accessible for personal use.
- The creator encourages others to try setting up and using the models locally, providing resources such as a GitHub guide and Python code for ease of use.
- Open-source models like deeps R1 are praised for driving down prices and increasing choices for users, pushing for more companies to share their work for broader access.
- The overall sentiment is positive towards the advancements and accessibility of open-source AI models, which benefit both individual users and the broader AI community.