Llama 3 Plus Groq CHANGES EVERYTHING!
TLDRDr. Know-It-All explores the synergy between the open-source Llama 3 model with 70 billion parameters and the high-speed Groq chip, capable of generating over 200 tokens per second. This combination allows for a new approach to Chain of Thought reasoning. The video showcases experiments with logic puzzles and math questions, leveraging Groq's speed to produce multiple answers and self-select the best one. This method is contrasted with the slower, single-shot responses from models like Chat GPT. The host credits Matthew Burman for inspiration and discusses the potential of this technology for solving complex problems, inviting viewers to share their thoughts on improving the pre-prompt and their own experiences with the combination of Groq and Llama 3.
Takeaways
- 🤖 The combination of the open-source Llama 3 model with the Groq chip, which can produce over 200 tokens per second, enables new possibilities in Chain of Thought reasoning.
- 🚀 Dr. Know-it-all credits Matthew Burman for inspiring the experiment and providing the questions, but also identifies some issues in the questions that might lead to incorrect answers.
- 💻 The Groq interface is easy to use, accessible via gro.com, and currently free of charge, which is highly appreciated.
- 🧠 Llama 3, particularly the 70 billion parameter model, is chosen for its superior capabilities among the open-source models available.
- 🔄 The experiment involves asking the model a logic puzzle and two math questions, then having the model generate 10 answers, self-reflect, and select the best one.
- 🎯 The speed of Groq allows for the generation of multiple answers and quick selection of the most accurate, which is not feasible with slower models like Chat GPT.
- 🧐 The model struggles with understanding physics and orientation in the logic puzzle about the marble and the cup, but eventually identifies the correct answer after producing multiple responses.
- 📉 A math question involving algebra is presented, and after an initial incorrect approach, the model, with a prompt correction, consistently provides the correct answer.
- 📈 The function f, involving a polynomial equation, is used to test the model's ability to solve for a constant. The model successfully identifies the correct value of C as -18 after correcting an input error.
- 🔢 Generating multiple answers and allowing the model to review them leads to a higher chance of solving complex problems that might not be achievable in a single attempt.
- 📝 The importance of tweaking the pre-prompt to improve the model's performance is emphasized, and feedback from the community is welcomed to refine the process.
- 📈 The collaboration of Groq and Llama 3 opens new ways to utilize large multimodal models, offering better answers than single-shot responses.
Q & A
What is the combination of Llama 3 and Gro that Dr. Know-It-All is discussing?
-The combination of Llama 3, an open-source model with 70 billion parameters, and Gro, a high-speed chip capable of producing over 200 tokens per second, is being discussed. This combination allows for a new approach to Chain of Thought reasoning.
How does the Gro platform work?
-Gro can be accessed by signing in with a Google account at gro.com. It is free to use and allows users to select different models, such as Llama 2 or the 70 billion parameter Llama 3, to generate responses.
What is the significance of the 10 answers approach used by Dr. Know-It-All?
-The 10 answers approach allows the model to generate multiple responses, review them, and select the best one. This enables the model to perform self-reflection, which is not possible with slower models like Chat GPT.
What is the logic puzzle that Dr. Know-It-All asks the Llama 3 model?
-The logic puzzle involves placing a small marble in an upside-down cup on a table, then placing the cup in a microwave without changing its orientation. The question is to determine where the marble is and to explain the reasoning step by step.
Why does Dr. Know-It-All believe that the orientation aspect of the logic puzzle trips up large multimodal models?
-The orientation aspect trips up these models because they do not have a physical embodiment and thus do not understand physics as well as a human being does. The models struggle with the concept of the cup being upside down and the effect of gravity on the marble.
What is the math question that Dr. Know-It-All corrects from Matthew Burman's original formulation?
-The corrected math question is '2/a - 1 = 4/y', where 'y' is not equal to zero and 'a' is not equal to one. The task is to find the value of 'y' in terms of 'a'.
What is the issue with the original function f defined by Matthew Burman?
-The issue is that the original function f defined as 'F of x = 2x^3 + 3x^2 + Cx + 8' with the x-axis intersection points including '(1/2, 0)' is incorrect due to a typo. The correct intersection point should be '(1/2, 0)' instead of '(120, 0)'.
How does the Gro platform enable the testing of multiple answers?
-Gro's high speed allows it to generate multiple answers quickly, which can then be reviewed and the best one selected. This would not be feasible with slower platforms due to the time it would take to generate and review multiple responses.
What is the value of C that Dr. Know-It-All finds when testing the function f with Gro?
-The value of C that Dr. Know-It-All finds when testing the function f is -18, which is the correct value after correcting the typo in the original function definition.
What is the potential improvement Dr. Know-It-All suggests for the pre-prompt to enhance the model's performance?
-Dr. Know-It-All suggests that the pre-prompt may need to be tweaked or a new prompt added after the question to ensure the model examines all the generated answers with a critical eye and selects the most accurate one in a single shot.
Why is the Gro platform considered a potential game-changer for using large multimodal models?
-The Gro platform is considered a game-changer because its high-speed token production allows for the generation and review of multiple answers quickly, leading to better responses than what can be achieved with single-shot answers in traditional interfaces.
Outlines
🚀 Introduction to Llama 70 Billion and Gro Chip
Dr. Know-It-All introduces the combination of the open-source Llama 70 billion parameter model and the Gro chip, which is capable of producing tokens at over 200 tokens per second. This combination allows for a new approach to Chain of Thought reasoning. The video credits Matthew Burman for inspiring the experiment and providing initial questions. Dr. Know-It-All plans to test the model with a logic puzzle and two math questions, using the Gro interface for its speed and cost-effectiveness. The process involves generating 10 answers, reviewing them, and selecting the best one, which is a form of self-reflection for the model.
🧐 Experimenting with Chain of Thought Reasoning
The video demonstrates the use of the Gro platform to solve a logic puzzle regarding a marble in an upside-down cup placed in a microwave. Initially, the model incorrectly answers the question, but upon correction, it identifies the correct answer from the list of 10 generated answers. The video also addresses a math problem, correcting an error in the original question posed by Matthew and successfully finding the correct answer using the Gro platform. The experiment shows that generating multiple answers and allowing the model to review them can lead to solving complex problems that are challenging with single-shot answers. The speed of Gro is highlighted as a key factor in enabling this type of reasoning.
🔧 Final Thoughts and Call for Feedback
Dr. Know-It-All concludes the video by emphasizing the potential of using Gro with Llama 3 to generate better answers than single-shot responses. He thanks Matthew Burman for the inspiration and invites viewers to share their thoughts on the pre-prompt, suggest improvements, and discuss their own experiments with the combination of Gro and Llama 3. The video ends with a call to like, subscribe, and look forward to the next video.
Mindmap
Keywords
💡Llama 3 Plus
💡Groq
💡Chain of Thought reasoning
💡Open Source
💡Multimodal models
💡Self-reflection
💡Logic Puzzle
💡Math questions
💡Pre-prompt
💡Tokens
💡Inference time
Highlights
Combining the Llama 70 billion parameter open-source model with the Gro high-speed chip allows for over 200 tokens per second, which is a game-changer for Chain of Thought reasoning.
Gro's interface is user-friendly, accessible via any Google account, and free to use.
The 70 billion parameter model is chosen for its superior performance among currently available open-source models.
Experimentation with logic puzzles and math questions reveals the potential of the Llama 3 and Gro combination.
The Gro platform enables the production of 10 answers instead of one, allowing the model to self-reflect and select the best answer.
Gro's speed allows for Chain of Thought reasoning, which is not feasible with slower models like Chat GPT.
The logic puzzle involving a marble and a cup tests the model's understanding of physics and orientation.
The model initially struggles with the marble and cup puzzle due to a lack of embodied understanding of physics.
After producing 10 answers, the model correctly identifies the marble as remaining inside the cup even after being placed in a microwave.
The model's self-correction mechanism, facilitated by Gro's speed, allows it to identify the correct answer among multiple responses.
A math question involving algebra is used to test the model's ability to solve complex problems.
The model provides multiple answers to the algebra question, with one of them being correct after a single correction.
The function f, defined by F(x) = 2x^3 + 3x^2 + CX + 8, is used to find the value of the constant C, where the graph intersects the x-axis at three points.
The model successfully calculates the value of C as -18 after generating multiple answers and self-evaluating them.
The use of Gro in conjunction with Llama 3 opens up new possibilities for using large multimodal models to generate better answers.
The video credits Matthew Burman for inspiring the experiment and discusses the need for further tweaking of the pre-prompt for optimal results.
Viewers are encouraged to provide feedback on the pre-prompt and share their own experiments with the Llama 3 and Gro combination.
The video concludes by emphasizing the transformative potential of Gro and Llama 3 for solving complex problems through rapid generation and self-evaluation of multiple answers.