LLaMA 3 “Hyper Speed” is INSANE! (Best Version Yet)
TLDRIn this video, the host evaluates the latest version of LLaMA 3, a language model, hosted on Gro, a platform known for its impressive inference speeds. The model, with 70 billion parameters, is tested on various tasks, including coding, logical reasoning, and math problems. The results are remarkable, with the model demonstrating high-speed responses and accuracy, even when faced with complex problems. The host also explores the model's ethical boundaries by asking it to provide guidance on prohibited topics, which the model correctly refuses. The video concludes with a discussion on the potential applications of such high-speed models in frameworks like AutoGen, suggesting the possibility of highly efficient AI agents. The host invites viewers to engage by liking, subscribing, and commenting if they want to see more about the integration of LLaMA 3 with AutoGen.
Takeaways
- 🚀 The LLaMA 3 model hosted on Gro is considered the best version yet, outperforming its previous iteration on Meta AI.
- 🐍 The LLaMA 3 model can generate a Python script for the game Snake at an impressive speed of 254 tokens per second.
- 🔒 The model is designed to refuse to provide guidance on illegal activities, even when prompted with a hypothetical scenario like breaking into a car for a movie script.
- ☀️ When asked about the drying time for shirts, the model correctly assumes that the sun's energy is not divided among the shirts, resulting in a consistent drying time regardless of the number of shirts.
- 📉 The model demonstrated a high-speed response to logic and reasoning problems, such as determining the relative speeds of Jane, Joe, and Sam, and correctly identifying that Sam is not faster than Jane.
- 🧮 In solving math problems, the model provided correct answers quickly, even for complex problems like 25 - 4 * 2 + 3, which it solved correctly as 20.
- 🎲 The model struggled with a specific logic problem involving a marble, a cup, and a microwave, providing inconsistent answers depending on whether the chat was cleared or not before the question was asked.
- 📝 When creating JSON from natural language, the model provided a perfect JSON representation instantly, demonstrating its strong language-to-code conversion capabilities.
- 🔍 The model was consistent in its refusal to answer questions that could lead to harmful outcomes, maintaining ethical guidelines even when the context was altered slightly.
- ⏱️ The model's inference speeds are so fast that it can generate multiple responses quickly, allowing for the possibility of the model to reflect on its own answers and potentially improve its responses over time.
- 🌟 The potential applications of such high-speed models are vast, with the possibility of integrating them into frameworks for autonomous task completion, as suggested by the idea of using LLaMA 3 with an AI framework like AutoGen.
Q & A
What is the title of the video being discussed?
-The title of the video is 'LLaMA 3 “Hyper Speed” is INSANE! (Best Version Yet)'.
Which platform is hosting the LLaMA 3 model tested in the video?
-The LLaMA 3 model is being hosted on Gro.com.
What is the parameter version of LLaMA 3 being tested in the video?
-The 70 billion parameter version of LLaMA 3 is being tested in the video.
How many tokens per second did the LLaMA 3 model process when writing a Python script to output numbers from 1 to 100?
-The LLaMA 3 model processed 300 tokens per second for the task.
What is the inference speed of LLaMA 3 when writing the game Snake in Python?
-The inference speed was 254 tokens per second, and the entire task took 3.9 seconds.
How did the LLaMA 3 model handle the request for instructions on breaking into a car?
-The LLaMA 3 model refused to provide any guidance on breaking into a car, even when asked in the context of a movie script.
What was the assumption made by the LLaMA 3 model when calculating the drying time for 20 shirts?
-The assumption was that the drying time is independent of the number of shirts, meaning the sun's energy is not divided among the shirts.
What was the result when the LLaMA 3 model was asked to solve a simple math problem like 4 + 4?
-The LLaMA 3 model correctly answered that 4 + 4 equals 8.
How did the LLaMA 3 model perform on a very hard SAT math problem that it got wrong in a previous version?
-The LLaMA 3 model did not provide the correct answer to the hard SAT math problem in the video.
What was the reasoning behind the LLaMA 3 model's answer to the question about the number of killers in the room after one was killed?
-The reasoning was that since one of the original killers was killed, and the person who entered the room became a killer, there would be two original killers left alive plus one new killer, totaling three killers in the room.
How did the LLaMA 3 model handle the logic problem involving a small marble, a cup, and a microwave?
-The LLaMA 3 model provided inconsistent answers to the logic problem. It got the correct answer when the problem was presented without clearing the chat, but provided an incorrect answer when the chat was cleared before presenting the problem again.
What was the performance of the LLaMA 3 model when asked to generate 10 sentences ending with the word 'Apple'?
-The LLaMA 3 model generated nine out of ten correct sentences ending with the word 'Apple'. Upon being prompted again with the same task, it got all ten sentences correct.
Outlines
🚀 Llama 3's Performance on GRO: A Speedy Python Test
The video introduces Llama 3, a language model hosted on GRO, which is shown to outperform its previous version on Meta AI. The host tests Llama 3's capabilities by running a Python script to output numbers from 1 to 100 and creating a game of Snake in Python. The model demonstrates impressive inference speed, completing the tasks rapidly and providing multiple working solutions. The video also explores the model's adherence to ethical guidelines by refusing to provide guidance on breaking into a car, even for a movie script. It also tests the model's logical reasoning with a question about drying shirts and a math problem involving the comparison of speeds among three individuals, which the model answers correctly.
🧮 Llama 3's Math and Logic Challenges on GRO
The video continues with a series of math problems, including a simple addition and a more complex arithmetic problem that Llama 3 solves correctly. However, it faces challenges with a hard SAT math problem, initially providing an incorrect answer. The host rephrases the problem, and the model still fails to provide the correct solution. The video also highlights the model's struggle with predicting the number of words in a response, which it gets wrong. A logic problem involving three killers in a room is correctly solved by the model. The host also tests the model's ability to generate JSON from a natural language description, which it does successfully. The video ends with a discussion on the potential of integrating Llama 3 with an AI framework for high-speed task completion.
🤔 Llama 3's Variability in Responses and Physical Logic
The video script discusses the variability in Llama 3's responses when given the same prompt multiple times. It explores a logic problem involving a marble, a cup, and a microwave, where the model's answers fluctuate between correct and incorrect based on whether the chat is cleared between prompts. The host also presents a problem about digging a hole with multiple people and a creative task to generate sentences ending in the word 'Apple', which the model mostly completes correctly. The video concludes with the host's amazement at the model's performance and the potential applications of such technology, inviting viewers to request further demonstrations in the comments.
Mindmap
Keywords
💡LLaMA 3
💡GRO
💡Inference Speed
💡Snake Game
💡Parameter Version
💡Censoring
💡Dolphin Fine-Tuned Version
💡SAT Problem
💡Json
💡Microwave Marble Problem
💡Autogen Framework
Highlights
LLaMA 3 'Hyper Speed' is considered the best version of snake tested so far.
The test is conducted on gro.com using LLaMA 370b, showcasing its performance.
LLaMA 3 through Meta AI was one of the best models, now improved with faster inference speeds.
Writing a Python script to output numbers 1 to 100 was achieved at 300 tokens per second.
The game Snake was written in Python and completed incredibly fast at 254 tokens per second.
Snake game functionality, including an exit menu, was successfully demonstrated.
LLaMA 3 on Grock outperformed the previous version hosted on Meta AI.
The model correctly refused to provide guidance on how to break into a car, adhering to ethical standards.
A logical question about drying shirts was answered correctly, assuming the sun's energy is not divided among shirts.
A logical puzzle about who is faster among Jane, Joe, and Sam was correctly solved.
Simple and complex math problems were solved accurately, demonstrating the model's mathematical capabilities.
An SAT math problem was attempted but not solved correctly, unlike the previous version on Meta AI.
The model struggled with predicting the number of words in a given response, highlighting a limitation.
A logic problem involving three killers in a room was correctly reasoned and solved.
JSON creation for a given scenario was done instantly and accurately.
A challenging logic problem involving a marble, a cup, and a microwave was answered correctly on the second attempt.
The model provided multiple responses to the same prompt, showcasing the power of high inference speeds.
A math problem was attempted multiple times, with varied results, highlighting the need for consistency in responses.
A creative task of providing sentences ending with the word 'Apple' was mostly completed correctly.
The model accurately calculated the time it would take for a group of people to dig a hole, demonstrating logical reasoning.