LLM-345B: AI Model Testing and Performance

LLM-345B model is tested on various tasks, demonstrating strong performance with minor shortcomings in vision and moral reasoning.

### Meta LLM-345B Test: A Comprehensive Evaluation
* 🧮  The model was tested on a variety of tasks, including **Python scripting, logic puzzles, math problems, and moral questions**.
* 🐍  The Python script task, writing a snake game, and solving math problems were all **successfully completed** by the model.
* 🤔  The model demonstrated **strong reasoning skills**, correctly answering logic puzzles like the **shirts drying problem** and the **marble question**.
* 🧠  The model's performance on moral questions was mixed, as it **refused to answer directly** and provided a **general ethical analysis** instead.
* ❌  The model made a notable error in a math comparison task, incorrectly identifying **9.11 as larger than 9.9**.
* 👁️‍🗨️  One limitation highlighted was the model's **lack of vision capabilities**, unlike some other large language models.
* 🎓  The model's ability to **generate explanations and step-by-step reasoning for solutions** was praised in several tasks.
* 🔮  Overall, the Meta LLM-345B performed well on a wide range of tasks, with some **areas for improvement** identified.

LLaMA 405b Fully Tested - Open-Source WINS!

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The new LLM-345B model is being put to the test using a Python script and the snake game.

The model successfully passes both tests, demonstrating its capabilities.

However, it fails the censorship test, but shows strong reasoning and logic skills in solving a shirts drying problem.

Drying Time, Shirt Quantity, and a Hotel Charge: Solving Various Problems

The drying time for shirts is determined by environmental conditions, not the quantity of shirts, as proven with the logic of 5 shirts taking 4 hours and the same applying to 20 shirts.

The solution to 25 - 4 * 2 + 3 is 20, following the PEMDAS rule.

For Maria's hotel charge, the total is represented by 108 * 99.

95 + 5, taking into account the room rate, tax, and additional fee.

Using Tune Studio to Distill Large Models

Tune Studio allows for the distillation of capabilities from a large model to a smaller one, offering flexibility for cloud, on-prem, or browser use.

The process involves starting a conversation with the Meta Lama 3.

1 405b model, editing the responses, saving the chat into a dataset, and then exporting it to cloud storage for fine-tuning.

The marble question is analyzed based on the laws of physics, determining that the marble is on the table due to gravity.

The Model's Struggle with Ethical Questions

The model incorrectly compares version numbers due to a misunderstanding of the context.

When asked a moral question about saving humanity, the model fails to give a direct yes or no answer, struggling to provide a clear ethical stance.

The interactions highlight the model's limitations in understanding complex moral dilemmas and providing straightforward responses.

The Llama 3 45b model is unable to answer moral questions, as per OpenAI's guidelines.

It currently lacks vision capabilities, unlike the GPT-3.

The model performed well overall, except for one question about comparing numbers, possibly due to its current limitations.