Llama 3.1 8B: Quality Testing and Vulture Partnership

The llama 3.1 8B model shows a significant quality improvement, especially doubling the Human Eval benchmark score.

### Llama 3.18B Quality Benchmark: A Disappointing Performance
* 🦙  The newly released Llama 3.18B model, with **8 billion parameters**, was expected to deliver significantly improved quality compared to the previous **405 billion parameter version**.
* 📊  Quality benchmarks showed only marginal improvements in some areas, with the Human Eval benchmark **doubling from 34 to 68** but other benchmarks showing **smaller gains**.
* 🚀  The model's speed and performance were highlighted as strengths, running Python code and **answering questions quickly**.
* 🔒  The model's censorship capabilities were tested, with **some success in censoring sensitive content** but also **some inconsistencies**.
* 🧠  The model's logic and reasoning abilities were tested, with mixed results. It correctly solved some problems but struggled with others, such as **lateral thinking puzzles**.
* 🤖  The model's response to moral dilemmas, such as the trolley problem, was evaluated. It **refused to provide a direct answer**, prompting a discussion on **AI's role in making moral judgments**.
* 💻  The model was hosted on Vulture's servers, which was praised for its **speed and performance**, despite the model's **quality shortcomings**.
* ❌  Overall, the performance of the Llama 3.18B model was deemed **disappointing**, with the reviewer expressing **high hopes that were not met** in this evaluation.

Llama 8b Tested - A Huge Step Backwards 📉

Artificial Intelligence (AI), Open Source, Generative Art, AI Art, Futurism, ChatGPT, Large Language Models (LLM), Machine Learning, Technology, Coding, Tutorials, AI News, and more

** Exclusive Pinecone Partner **

Matthew Berman

The new 8 billion parameter version of the Llama 3.

1 model has doubled the quality, which is a significant improvement.

The 8B model is being powered by Vulture and can be set up to run through open web UI, offering an easy way to run models locally or using Vulture for larger models.

The performance benchmarks of the Llama 3.

1 8B model show improvements across the board, with some scores doubling compared to the previous version.

Issues with Running a Python Script and Snake Game

The Python script provided multiple versions for outputting numbers 1 to 100, with the final version being successful.

However, the Snake game code had several issues, including the snake not moving properly and leaving a trail that caused the game to fail.

The video includes sponsored content from Vulture.

The math problems presented are easily solved by the model, leading to disappointment.

The model correctly answers a classic riddle about the number of killers left in a room, showcasing logical reasoning.

Ineffective Testing of Large Language Models

The marble remains inside the glass even when it's upside down, occupying the same physical space.

9, but both the 405 billion and 8 billion parameter models got it wrong.

The large language model fails to provide the correct answer to the morals question about saving humanity from extinction.

The Disappointing Performance of Llama 3.

The AI model was unwilling to make a moral judgment on pushing someone to save humanity from extinction.

The user is disappointed in the model's performance and is unsure about its usefulness.

The user plans to investigate other people's experiences with the Llama 3.