Testing llama 3 with Python 100 times so you don't have to

In this video, the creator tests the AI model Llama 3 by asking it the same question 100 times using Python and the olama package. The question involves a scenario where a cake is placed on a table in the dining room, a plate is put on top of the cake, and then the plate is taken into the kitchen. The AI is asked to identify the room where the cake is currently located. After several attempts and adjustments to the prompt, the AI model consistently identifies the 'dining room' as the correct answer, except for a few instances where it incorrectly chooses 'kitchen'. The video highlights the importance of crafting the correct prompt for AI language models and the potential for variability in responses. The creator concludes that with the right prompt, Llama 3 can provide accurate answers 98% of the time, demonstrating the model's reliability when given clear and specific instructions.

Takeaways

• 🤖 The video discusses testing the AI model 'Llama 3' 100 times using Python to observe variations in its responses.
• 📚 The presenter uses the `olama` package in Python to interact with the AI model locally on their machine.
• 🔍 The initial question posed to Llama 3 involves a scenario about a cake and a plate, aiming to determine the room the cake is in.
• 📈 Through multiple iterations, the presenter finds that Llama 3 correctly identifies the 'dining room' as the location of the cake 98% of the time.
• 🔧 The process involves tweaking the prompt to get the desired response format, such as a single letter answer (A or B).
• 💻 The presenter uses a loop to automate the questioning process and collect responses.
• 📊数据分析 reveals that the model's accuracy significantly improves when given clear and specific instructions.
• 🔠 The presenter experiments with different prompting techniques to get consistent answers from the AI model.
• 🔄 A loop is implemented to repeat the question 100 times to gather a comprehensive set of responses.
• 📋 The final step involves tallying the responses to determine the model's accuracy.
• 🎯 The key takeaway is the importance of crafting the correct prompt when interacting with large language models to ensure accurate responses.

Q & A

What was the initial question posed to Llama 3 in the previous video?

The initial question was about a scenario where a person places a plate on top of a cake in the dining room, picks up the plate, and takes it into the kitchen. The question was to determine in which room the cake was after this action.

What was the outcome of the initial question in the previous video?

In the previous video, Llama 3 appeared to have answered incorrectly, suggesting the cake was in the kitchen, while Llama 53 seemed to have answered correctly, stating the cake remained in the dining room.

How does the olama package allow interaction with large language models?

The olama package provides a means to interact with large language models by including its own web server and Python bindings, enabling users to install the package, specify the model, and start asking questions directly from their local machine.

What was the purpose of running the same question to Llama 3, 100 times?

The purpose was to observe how the answers might vary each time the question was asked, to understand the consistency and reliability of the model's responses.

What was the final outcome of asking the question 100 times?

After asking the question 100 times, Llama 3 answered correctly (the dining room) 98% of the time, indicating a high level of accuracy once the correct prompt was established.

Why is crafting the correct prompt important when interacting with large language models?

Crafting the correct prompt is important because it significantly influences the model's understanding and the accuracy of its response. An improper prompt can lead to incorrect or less relevant answers.

What was the role of Python in automating the process of asking the question 100 times?

Python was used to automate the process by installing the olama package, defining the question within a script, and using a loop to repeat the question 100 times, thus efficiently gathering a large sample of responses.

What was the issue encountered when attempting to get a one-letter answer (A or B) from Llama 3?

The issue was that Llama 3 struggled to consistently provide a one-letter answer and seemed to default to one option over the other depending on the phrasing of the prompt, leading to inconsistent results.

How did the use of a multiple-choice format affect the model's responses?

When the question was formatted as a multiple-choice query with clear options, Llama 3 provided the correct answer (A for dining room) 100% of the time when asked repeatedly, showing the importance of clear and structured prompts.

What was the conclusion drawn from the experiment of asking the question 100 times?

The conclusion was that with the right prompting, Llama 3 can provide accurate answers most of the time. However, when the correct answer is not known in advance, relying solely on the model's response can be problematic.

What does the experiment suggest about the reliability of large language models in providing answers?

The experiment suggests that large language models can be highly reliable when given clear and structured prompts, but their reliability may vary when the correct answer is not already known to the person crafting the prompt.

What was the significance of the experiment in understanding the behavior of large language models?

The experiment highlighted the importance of prompt crafting and the potential variability in responses from large language models. It also demonstrated how these models can learn and adjust their responses based on the input provided.

Outlines

00:00

🤖 Automating Queries with Python and LLMs

The speaker discusses their previous video where they posed a question to two different versions of a language model, LLM 3 and LLM 53, and found varying responses. They express a desire to ask the same question to LLM 3 multiple times to see the variation in answers. To do this, they use the 'olama' package in Python, which allows interaction with large language models through a web server and Python bindings. They demonstrate the installation process and how to use the package to ask questions and receive responses. The main question revolves around a scenario involving a cake, a table, and a plate, aiming to determine which room the cake is in based on the actions described.

05:02

🔁 Experimenting with Model Responses

The speaker continues their exploration by manually adjusting the query to receive a binary (A or B) response from the model. They observe that the model's answer changes based on slight variations in the prompt, sometimes correctly identifying the room as 'dining room' and other times incorrectly as 'kitchen'. They then decide to automate the process by looping the question 10 times to see the consistency of the model's responses. The speaker notes the importance of crafting the right prompt for these large language models and expresses curiosity about the model's behavior when the correct answer is not known in advance.

10:04

📈 Analyzing Model Consistency

After manually testing the model's responses, the speaker decides to run the query 100 times to assess the model's consistency. They implement a loop in Python to automate the process and collect the responses into a list called 'answers'. The speaker then analyzes the collected data, counting the occurrences of each answer ('A' for dining room and 'B' for kitchen) and introduces a 'no answer' category for cases where the model fails to provide a clear choice. The analysis reveals that the model correctly identifies the answer 98% of the time, which the speaker finds satisfactory despite some initial confusion.

15:06

🔍 Reflecting on AI Reliability

The speaker concludes by reflecting on the reliability of large language models when the correct answer is not known in advance. They caution against relying solely on these models without proper testing and prompt crafting. The speaker acknowledges that their prior critique of LLM 3 in a previous video may have been hasty, as further testing showed the model to be correct 98% of the time in this scenario. They encourage viewers to subscribe for more content on Python, problem-solving, and working with large language models, and sign off with a promise to continue exploring these topics in future videos.

Keywords

Llama 3

Llama 3 refers to a large language model developed by Microsoft. In the video, the creator is testing the model's ability to answer a specific question by interacting with it 100 times to see the variation in responses. It is a central focus as it determines the effectiveness and reliability of the model under scrutiny.

Python

Python is a high-level programming language used in the video for automating the process of asking questions to the Llama 3 model. The script demonstrates how to use Python with the 'olama' package to interact with the language model locally, which is crucial for the experiment's execution.

Olama

Olama is a package or tool mentioned in the video that facilitates interaction with language models like Llama 3. It comes with a web server and Python bindings, allowing the user to install it and start asking questions to the model directly from their local machine.

Web Server

A web server in this context is a software and hardware combination that hosts websites and serves web pages to users over the internet. Olama's web server allows the local interaction with the Llama 3 model, which is a key component in the testing process described in the video.

Language Model

A language model is a type of artificial intelligence used for understanding and predicting language. Llama 3 is an example of a large language model, which is being tested for its accuracy and consistency in answering a given question in the video.

Message Content

In the context of the video, message content refers to the specific question or prompt given to the Llama 3 model. The content is crucial as it dictates the model's response and is the subject of the 100 iterations of questioning performed in the experiment.

Multiple Choice Response

A multiple choice response is a type of answer format where the respondent is given several options to choose from. In the video, the creator attempts to get the Llama 3 model to provide answers in this format to simplify the evaluation process.

Loop

In programming, a loop is a sequence of instructions that is continually repeated until a certain condition is reached. The video involves using a loop in Python to ask the same question to Llama 3 multiple times, which is essential for gathering a large sample of responses for analysis.

Data

Within the video, data refers to the responses collected from the Llama 3 model. The discussion points out that in the future of AI and language models, data may not just be numbers but also language, emphasizing the shift towards understanding and processing linguistic information.

Prompting

Prompting in the context of the video is the act of formulating the right question or instruction to guide the Llama 3 model to provide the desired response. The effectiveness of the prompting is highlighted as a critical factor in the model's performance.

Accuracy

Accuracy is the degree to which the Llama 3 model's responses align with the expected or correct answer. The video focuses on testing the model's accuracy by asking the same question multiple times and analyzing the consistency of the responses.

Highlights

The video explores testing Llama 3 with Python 100 times to observe variations in its answers.

Olama package is used for interaction with large language models through a local web server and Python bindings.

The process involves installing Olama via pip and using it to ask questions and receive responses in Python.

The original question from a previous video is used to test Llama 3's consistency in answering.

The question involves a scenario with a cake, a plate, and determining the room the cake is in.

Llama 3 initially provides a detailed response, which is then refined to a multiple-choice format.

The video demonstrates the importance of crafting the correct prompt for obtaining accurate answers from Llama 3.

After several iterations, Llama 3 correctly identifies 'dining room' as the answer 98% of the time.

The video emphasizes the need for multiple prompts and testing to achieve reliable outcomes from language models.

The experiment with Llama 3 shows that the model's accuracy can be influenced by the way questions are asked.

The video discusses the potential issue of relying on AI systems when the correct answer is not already known.

The author expresses satisfaction with Llama 3's performance after refining the questioning process.

The video concludes by encouraging viewers to subscribe for more content on Python and large language models.

The testing process reveals that Llama 3's responses can vary based on the phrasing of the question.

A loop is implemented to ask the same question 100 times and analyze the consistency of Llama 3's answers.

The video highlights the evolving nature of data analysis, shifting from numbers to language understanding.

The author suggests that further testing and refinement of prompts are necessary for more accurate AI model responses.

The video demonstrates the use of a list to store and analyze the model's answers from multiple iterations.

The final tally of answers shows that Llama 3 provided the correct answer 98 times out of 100.