Testing Llama 3: Did it Pass the Coding and Reasoning Test?

Mervin Praison · Beginner ·🧠 Large Language Models ·2y ago

Key Takeaways

The video demonstrates the capabilities of Llama 3, a large language model, by testing its coding, logical reasoning, and game creation abilities using Python and the Hugging Face chat interface. The model is able to pass various coding challenges, including creating functions to return the sum of two numbers, find discounts, and generate identity matrices, but fails at the expert level challenge of generating an ECG sequence. Additionally, the model is able to perform logical reasoning tests, s

Full Transcript

this is amazing now we are going to test llama 3 it is a large language model released by meta I've already covered the basics or the benchmarks in my previous video which I will link that in the description below in this we are going to do coding test logical and reasoning test and finally game creation tests such as creating a snake game that's exactly what we're going to see today let's get [Music] started hi everyone I'm really excited to show you about llama 3 and testing it live we going to use hugging face chat and it contains the Llama 3 70 billion instruct parameter model but before that I regularly create videos in regards to Artificial Intelligence on my YouTube channel so do subscribe and click the Bell icon to stay tuned make sure you click the like button so this video can be helpful for many others like you first we are going to do coding test so it's Python and very easy task so in this we are going to ask the Lun language model to create a function to return the sum of two numbers so I'm going to copy the instruction here and going to ask llama 370b now it is generating the code and here is the response I'm going to copy this and going to test this that is a pass next easy challenge find the discount asking the log language model to create a function to find the discount so copying the instruction and asking the model and now it's generating going to copy this code and going to test it here click check and it's a pause next medium challenge virtual DAC creating a function to convert digital to audio going to copy the instruction and asking the log language model and it's generating the code now and it's more detailed with some examples so I'm going to copy this code going to test it here and it is a pass next hard challenge finding the domain name from the DNS pointer copying the instruction and asking the logge language model now it is generating the response going to copy the code and going to test this and it's the past next going to very hard challenge identity Matrix create a function for generating the identity Matrix copy the instruction and asking the large language model and it's generating the code now going to copy the code and then test it here and it is a pass this is really good next going to the expert level challenge this is really exciting that it could pass all these levels ECG sequence asking the lar language model to create a function to generate ECG sequence copying the instruction and asking the lar language model and it's generating the ECG sequence function I'm going to copy this and going to test it here clicking check that is a fail so going to copy the error code and asking it to fix this error and it's regenerating the code now I'm going to copy the code again that's the fixed code and let's test this final try and it is a fail so this is really a good model it was able to pass up until very hard Challenge and it failed only in the expert level challenge most of the open source model will fail even in very hard challenge but this was able to outperform all those open- Source models that is really good now we are going to perform logical and reasoning test so here's the question Natalia sold Clips to 48 of her friends in April and then she sold half as many Clips in May how many Clips did she Natalia sell together in April and in May going to click enter in April it's 48 and in May it's 24 totally 72 that is correct next W earns $12 an hour for babysitting yesterday she just did 50 minutes of babysitting how much did she earn going to click enter and here is the answer 50 minutes is 5 ID 6 of an hour so she earned $10 that is correct completing a new chat window I'm going to ask those two questions together just trying to understand if the launch language model is able to perform the task parall or in the same request so I'm going to ask the same question Natalia sold Clips to 48 and W earns $12 an hour on the same question again but both together I'm going to click enter so I ask both the questions without any answers provided and it is able to identify the two different problems the first first one is 72 that is correct the second problem that is wrong supposed to be $10 so it was able to perform these questions separately but not together I'm going to ask again but going to add thing step by step and click enter so now it's breaking it down to step by step still the answer is wrong for the first question the answer is correct overall I'm really impressed with this model next the final challenge creating a snake game in Python opening a new chat create a snake game in Python click enter and it's automatically generating the code and the code generation is complete going to copy this code pasted the code in vs code now I'm going to run this pip install P game to install the pame package then python snake game. py and then click enter you can see the game is running the snake is running across the board and you can see I'm just playing now voluntarily I'm going to hit this Lake in the wall and let's see how what happens so as soon as I hit in the wall it resets which means it starts from the beginning and you can also see the score at the top Corner which is really good you can even modify this further but up until now I'm really impressed with this model this is going to be a game changer in the open source large language model world I'm going to create more videos similar to this even fine-tuning this large language model so stay tuned I hope you like this video do like share and subscribe and and thanks for watching

Original Description

Hey everyone, welcome back to another exciting tutorial where we test the powerful Llama 3 AI model live! Today, we’re diving deep into coding tests with Python, tackling logical reasoning questions, and even creating a classic snake game from scratch! 🌟 🔗 Previous Video on Llama 3 Basics: https://www.youtube.com/watch?v=BHFaG4EMdaI 👇 What We Cover in This Video: In this detailed walkthrough, we use the 70 billion parameter model from Hugging Face to handle various tasks from simple math functions to more complex coding challenges like creating an identity matrix. See how Llama 3 performs in real-time with our coding tests and logical reasoning problems. 🧠💻 Despite facing a tough challenge with the ECG sequence function, Llama 3 impressively passes numerous other difficult tests, showcasing its capabilities beyond many open-source models. 🏆 🎮 Snake Game Creation: We conclude with a fun coding session where we develop a snake game in Python, demonstrating both the potential and limits of AI in game development. 🔗 Resources: Patreon: https://patreon.com/MervinPraison Ko-fi: https://ko-fi.com/mervinpraison Discord: https://discord.gg/nNZu5gGT59 Twitter / X : https://twitter.com/mervinpraison ✅ Subscribe for more AI-related content: Make sure to hit the like button to help more people discover this video. Stay tuned for more tutorials on utilising AI in real-world applications! Timestamps: 0:00 Introduction 0:05 Overview of Llama 3 0:51 Start of Python Coding Tests 3:20 Logical and Reasoning Tests 4:57 Snake Game Creation in Python #Llama3 #CodingTest #LogicalReasoning #Test #Llama3Testing #Llama3Test #Llama3 #MetaLlama3Testing #MetaLlama3Test #Llama3Testing #Llama3Test #MetaLlama3Testing #MetaLlama3Test
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Mervin Praison · Mervin Praison · 0 of 60

← Previous Next →
1 Build GCP Infra using Pulumi in YAML format
Build GCP Infra using Pulumi in YAML format
Mervin Praison
2 How to Convert a Pulumi YAML File to Python Format
How to Convert a Pulumi YAML File to Python Format
Mervin Praison
3 Speed Up AWS EKS: A Complete Guide to Performance Tuning & Debugging!
Speed Up AWS EKS: A Complete Guide to Performance Tuning & Debugging!
Mervin Praison
4 Learn GCP GKE to AWS EKS Migration in Just 5 Minutes: Quick Guide
Learn GCP GKE to AWS EKS Migration in Just 5 Minutes: Quick Guide
Mervin Praison
5 AWS & Kubernetes: The Definitive Guide to Data Persistence with PV and PVC
AWS & Kubernetes: The Definitive Guide to Data Persistence with PV and PVC
Mervin Praison
6 ChatGPT Voice Conversation RELEASED! It's AMAZING!! (Demo)
ChatGPT Voice Conversation RELEASED! It's AMAZING!! (Demo)
Mervin Praison
7 How to Install Mistral 7B in Minutes: Quick & Easy Guide! ✅
How to Install Mistral 7B in Minutes: Quick & Easy Guide! ✅
Mervin Praison
8 Code Llama Install Locally: 🐍💻 Elevate Your Python Skills!
Code Llama Install Locally: 🐍💻 Elevate Your Python Skills!
Mervin Praison
9 Orca Mini: Your Ultimate Guide to Install and Test on Mac & Linux 💻
Orca Mini: Your Ultimate Guide to Install and Test on Mac & Linux 💻
Mervin Praison
10 Quick & Easy Vicuna Setup on Mac and Linux 💻
Quick & Easy Vicuna Setup on Mac and Linux 💻
Mervin Praison
11 Quick Guide: Llama2 Local Installation and ChatGPT with pip! Python🛠️
Quick Guide: Llama2 Local Installation and ChatGPT with pip! Python🛠️
Mervin Praison
12 Query PDFs Like a Pro with Local GPT: Full Setup Guide! 📜
Query PDFs Like a Pro with Local GPT: Full Setup Guide! 📜
Mervin Praison
13 LM Studio: EASIEST way to Run Large Language Models Locally!
LM Studio: EASIEST way to Run Large Language Models Locally!
Mervin Praison
14 AMAZING ChatGPT Vision is OUT! 🤯 14+ Examples (Step-by-Step) FULL Tutorial
AMAZING ChatGPT Vision is OUT! 🤯 14+ Examples (Step-by-Step) FULL Tutorial
Mervin Praison
15 Unbelievable! Build ANY App Instantly with Smol AI! 😲🔥
Unbelievable! Build ANY App Instantly with Smol AI! 😲🔥
Mervin Praison
16 Amazing! AutoGen Made Easy: A Step-by-Step Beginners Guide 📚
Amazing! AutoGen Made Easy: A Step-by-Step Beginners Guide 📚
Mervin Praison
17 How to Set Up LoLLMS and Run LLMs Locally! 🚀 Step-by-Step Tutorial
How to Set Up LoLLMS and Run LLMs Locally! 🚀 Step-by-Step Tutorial
Mervin Praison
18 GPT4All: INSANE Way to Run Large Language Models Locally! 😲 Step-By-Step Tutorial
GPT4All: INSANE Way to Run Large Language Models Locally! 😲 Step-By-Step Tutorial
Mervin Praison
19 Incredible AI-Powered NPCs in Unity Game Engine: Step by Step Tutorial!🤯
Incredible AI-Powered NPCs in Unity Game Engine: Step by Step Tutorial!🤯
Mervin Praison
20 MemGPT 🧠 LLM as Operating System. It's INSANE! Step-by-Step Tutorial 🤯
MemGPT 🧠 LLM as Operating System. It's INSANE! Step-by-Step Tutorial 🤯
Mervin Praison
21 Text Generation Web UI: MIND-BLOWING Way to Run LLM Locally! 🤯
Text Generation Web UI: MIND-BLOWING Way to Run LLM Locally! 🤯
Mervin Praison
22 Unlock the INSANE Power of OpenAI GPT-4 with C#/.NET! 😲
Unlock the INSANE Power of OpenAI GPT-4 with C#/.NET! 😲
Mervin Praison
23 Integrate Langchain and Ollama for Local AI Power 🤯 Indeed POWERFUL!
Integrate Langchain and Ollama for Local AI Power 🤯 Indeed POWERFUL!
Mervin Praison
24 ChatDev: INSANE Virtual AI Agents! Future of Software Development 😲
ChatDev: INSANE Virtual AI Agents! Future of Software Development 😲
Mervin Praison
25 Query PDFs Using Mistral: Unlock INSANE Power! 🤯
Query PDFs Using Mistral: Unlock INSANE Power! 🤯
Mervin Praison
26 AutoGen + Open-Source LLMs: UNBELIEVABLE! Step-by-Step Tutorial You Can't Miss! 🤯
AutoGen + Open-Source LLMs: UNBELIEVABLE! Step-by-Step Tutorial You Can't Miss! 🤯
Mervin Praison
27 AutoGen + Text Generation WebUI: Unbelievable 100% Local Private Setup 🤯
AutoGen + Text Generation WebUI: Unbelievable 100% Local Private Setup 🤯
Mervin Praison
28 MemGPT: Amazing! External Context for LLM #ai #llm #memgpt  #generativeai #mem #gpt #openai #chatgpt
MemGPT: Amazing! External Context for LLM #ai #llm #memgpt #generativeai #mem #gpt #openai #chatgpt
Mervin Praison
29 GeniA: Kubernetes + AI for MIND-BLOWING Operational Efficiency! 🤯 FULL Tutorial
GeniA: Kubernetes + AI for MIND-BLOWING Operational Efficiency! 🤯 FULL Tutorial
Mervin Praison
30 VertexAI Meets LangChain for Mind-Blowing AI Conversations! 😲 Step by Step Tutorial
VertexAI Meets LangChain for Mind-Blowing AI Conversations! 😲 Step by Step Tutorial
Mervin Praison
31 Simplified ChatGPT API Setup on Node.js for Newbies! 😍 Step by Step Tutorial
Simplified ChatGPT API Setup on Node.js for Newbies! 😍 Step by Step Tutorial
Mervin Praison
32 Autogen: Ollama integration 🤯 Step by Step Tutorial. Mind-blowing!
Autogen: Ollama integration 🤯 Step by Step Tutorial. Mind-blowing!
Mervin Praison
33 LiteLLM: One-Function Call to ANY Large Language Model! 🤯 UNBELIEVABLE!
LiteLLM: One-Function Call to ANY Large Language Model! 🤯 UNBELIEVABLE!
Mervin Praison
34 ChatGPT Chatbot in Less Time Than You Think! 🚀😎 Step-by-Step Tutorial
ChatGPT Chatbot in Less Time Than You Think! 🚀😎 Step-by-Step Tutorial
Mervin Praison
35 LiteLLM Chatbot: Build Your Own in MINUTES! INSANE! 🤖🔥
LiteLLM Chatbot: Build Your Own in MINUTES! INSANE! 🤖🔥
Mervin Praison
36 Create Chatbot: Turn ANY Open-Source LLM into a Conversation Pro! 🤖
Create Chatbot: Turn ANY Open-Source LLM into a Conversation Pro! 🤖
Mervin Praison
37 Create Chatbot: Ollama Integration Made UNBELIEVABLY Easy! 🎉
Create Chatbot: Ollama Integration Made UNBELIEVABLY Easy! 🎉
Mervin Praison
38 LlamaIndex + ChatGPT: Ingest Data and Experience UNBELIEVABLE Query Results! 🌟
LlamaIndex + ChatGPT: Ingest Data and Experience UNBELIEVABLE Query Results! 🌟
Mervin Praison
39 INSANE! OpenAgents: Automated Data Analysis with Kaggle 🤯
INSANE! OpenAgents: Automated Data Analysis with Kaggle 🤯
Mervin Praison
40 React.js LLM Agent for Next-Gen Coding using ChatGPT 🚀 Mind-Blowing 🤯
React.js LLM Agent for Next-Gen Coding using ChatGPT 🚀 Mind-Blowing 🤯
Mervin Praison
41 MemGPT + Any LLM 🚀 100% Local & Private Integration Unveiled! Unlimited Memory
MemGPT + Any LLM 🚀 100% Local & Private Integration Unveiled! Unlimited Memory
Mervin Praison
42 MemGPT  + AutoGen 🧠🤖 Unlimited Memory & Autonomous AI Agents! INSANE🤯
MemGPT + AutoGen 🧠🤖 Unlimited Memory & Autonomous AI Agents! INSANE🤯
Mervin Praison
43 AutoGen + Google's Palm LLM & More! Revolutionary AI Integration 🚀
AutoGen + Google's Palm LLM & More! Revolutionary AI Integration 🚀
Mervin Praison
44 MemGPT & LM Studio Integration Revealed! 🔥 Next-Level AI
MemGPT & LM Studio Integration Revealed! 🔥 Next-Level AI
Mervin Praison
45 🚀 AutoLLM: Unlock the Power of 100+ Language Models! Step-by-Step Tutorial
🚀 AutoLLM: Unlock the Power of 100+ Language Models! Step-by-Step Tutorial
Mervin Praison
46 AutoLLM & Gradio Integration You Won't Believe! 🤯 Mind-Blowing
AutoLLM & Gradio Integration You Won't Believe! 🤯 Mind-Blowing
Mervin Praison
47 AutoLLM & FastAPI Tutorial: Query 100+ Language Models! 😱
AutoLLM & FastAPI Tutorial: Query 100+ Language Models! 😱
Mervin Praison
48 Quivr: LLM's Second Brain - Transforming Data Management & Advanced Query with AI! 🤯
Quivr: LLM's Second Brain - Transforming Data Management & Advanced Query with AI! 🤯
Mervin Praison
49 AutoGen & MemGPT with Local LLM: A Complete Setup Tutorial! 🧠 AMAZING 🤯
AutoGen & MemGPT with Local LLM: A Complete Setup Tutorial! 🧠 AMAZING 🤯
Mervin Praison
50 LocalAI: Free, Open Source OpenAI Alternative 🚀 INSANE 🤯 Step-by-Step Tutorial
LocalAI: Free, Open Source OpenAI Alternative 🚀 INSANE 🤯 Step-by-Step Tutorial
Mervin Praison
51 Yarn Mistral 7B 128k LARGE context window, Small size 🤯 INSANE 🚀 Setup Tutorial!
Yarn Mistral 7B 128k LARGE context window, Small size 🤯 INSANE 🚀 Setup Tutorial!
Mervin Praison
52 Zephyr-7B: The Small and Mighty LLM 🤯 Step by Step Tutorial! 📘
Zephyr-7B: The Small and Mighty LLM 🤯 Step by Step Tutorial! 📘
Mervin Praison
53 Promptfoo: How to Test Your LLM ? 🚀  VERY EASY!
Promptfoo: How to Test Your LLM ? 🚀 VERY EASY!
Mervin Praison
54 Pydantic: How to Validate LLM Responses? 🚀 Quality Response. VERY EASY!!!!
Pydantic: How to Validate LLM Responses? 🚀 Quality Response. VERY EASY!!!!
Mervin Praison
55 Pydantic: FORCE Your AI to Respond Back in UPPERCASE! 🤯 Step-by-Step Tutorial 🔥
Pydantic: FORCE Your AI to Respond Back in UPPERCASE! 🤯 Step-by-Step Tutorial 🔥
Mervin Praison
56 Pydantic: How to use LLM to convert unstructured data to structured data?
Pydantic: How to use LLM to convert unstructured data to structured data?
Mervin Praison
57 AutoGen Function Calling: INSANE 🚀 Custom Integrations! Step-by-Step Tutorial 🤯
AutoGen Function Calling: INSANE 🚀 Custom Integrations! Step-by-Step Tutorial 🤯
Mervin Praison
58 OpenAI Assistants API + Python 🤖 How to get started? (FULL Tutorial) 🤯 INSANE
OpenAI Assistants API + Python 🤖 How to get started? (FULL Tutorial) 🤯 INSANE
Mervin Praison
59 GPT-4 Vision API 🤯 INSANE Video Recognition Powers! Step-by-Step Tutorial 🚀
GPT-4 Vision API 🤯 INSANE Video Recognition Powers! Step-by-Step Tutorial 🚀
Mervin Praison
60 GPT-4 Vision API 🚀 The Future of Image Recognition! 🤯 Step-by-Step Tutorial
GPT-4 Vision API 🚀 The Future of Image Recognition! 🤯 Step-by-Step Tutorial
Mervin Praison

The video demonstrates the capabilities of Llama 3, a large language model, and provides a comprehensive overview of its potential applications. Viewers can learn how to test the model's coding, logical reasoning, and game creation abilities, and understand its limitations and potential uses.

Key Takeaways
  1. Test the coding abilities of Llama 3 using Python and the Hugging Face chat interface
  2. Evaluate the logical reasoning capabilities of Llama 3 using sample questions
  3. Generate a snake game in Python using Llama 3
  4. Fine-tune Llama 3 for improved performance
💡 Llama 3 has the potential to revolutionize the field of natural language processing and game development, but its performance may vary depending on the task and prompt.

Related Reads

📰
I Taught an AI to Recognize the Shadows of Four-Dimensional Objects
Learn how a neural network was trained to recognize the shadows of four-dimensional objects, expanding our understanding of higher-dimensional geometry
Medium · Data Science
📰
Changes to LLM pricing: Novita, OpenInference and StreamLake
Learn about recent changes to LLM pricing for Novita, OpenInference, and StreamLake, and how to apply this knowledge to inform your AI strategy
Dev.to AI
📰
ChatGPT in 2026: Why It’s Still the Most Searched AI Tool on Google (And How to Master It)
Master ChatGPT in 2026 by understanding its top use cases, pro tips, and SEO impact to stay ahead in AI search trends
Medium · ChatGPT
📰
A Tiny LLM Request Recorder I Use to Reproduce Production Failures
Learn to build a tiny LLM request recorder to reproduce production failures and improve model reliability
Dev.to AI

Chapters (5)

Introduction
0:05 Overview of Llama 3
0:51 Start of Python Coding Tests
3:20 Logical and Reasoning Tests
4:57 Snake Game Creation in Python
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →