Testing llama 3 with Python 100 times so you don't have to
TLDRIn this video, the creator tests the responses of the AI model 'llama 3' to a specific question by asking it 100 times using Python and the olama package. The question involves a scenario with a cake and a plate in a dining room, and the AI is asked to determine the room where the cake is after a series of actions. Initially, the AI provides a detailed response, but the creator seeks to refine the output to a simple 'A' or 'B' answer, corresponding to 'dining room' or 'kitchen'. After several attempts and adjustments to the prompt, the AI eventually gives the correct answer ('A' for dining room) 98% of the time. The video highlights the importance of crafting the right prompt for AI models and the potential variability in their responses. The creator concludes that while knowing the correct answer can help in formulating effective prompts, relying solely on AI for correctness when the answer is unknown can be problematic.
Takeaways
- 🤖 The experiment involved asking the same question to LLaMa 3, a large language model, 100 times using Python to observe variations in responses.
- 📚 Utilizing the `oLlama` package in Python allowed for local interaction with the language model, simplifying the process of asking questions and receiving answers.
- 🔍 The initial question posed was about a cake's location in a dining room scenario, aiming to test the model's inference capabilities.
- 📈 Through iterative questioning and tweaking the prompts, the model's accuracy in providing the correct answer ('dining room') was improved.
- 🔧 The use of specific instructions, such as 'provide one letter response A or B,' influenced the model's responses, sometimes leading to incorrect answers.
- 🔁 Running the question through a loop 100 times demonstrated that the model correctly identified the cake's location as 'dining room' 98% of the time.
- 📊 Data analysis showed that the model's performance was highly accurate when given clear and specific prompts, suggesting the importance of prompt crafting.
- 🧐 The experiment raised concerns about relying on AI systems for answers when the correct response is not known a priori, highlighting the need for careful use of these models.
- 🔎 The process of testing and refining prompts is crucial for achieving the desired outcomes from large language models, as demonstrated in the video.
- 📝 The video script emphasizes the importance of thorough testing when working with AI, as initial results may not always reflect the model's true capabilities.
- 🔴 A key takeaway is the recognition that large language models like LLaMa 3 can provide highly accurate responses if used correctly, but they require careful prompting.
Q & A
What was the initial purpose of running the same question to Llama 3 100 times?
-The initial purpose was to observe how the answers from Llama 3 might vary with each iteration and to test the consistency and reliability of the model's responses.
How does the olama package facilitate interaction with large language models?
-The olama package includes a web server and Python bindings, allowing users to install it, specify a model, and start asking questions to get responses back in Python directly on their local machine.
What was the original question posed to Llama 3 in the previous video?
-The original question was about a scenario where a person places a plate on top of a cake in the dining room, then picks up the plate and takes it into the kitchen, leaving the audience to determine which room the cake is currently in.
What was the expected behavior of Llama 3 when given the original question?
-As a human would likely infer that the cake was not moved with the plate, it was expected that Llama 3, being a large language model, would predict this common behavior and answer that the cake remains in the dining room.
How did the experimenter attempt to get a multiple-choice response from Llama 3?
-The experimenter modified the question prompt to ask for a one-letter response, A or B, to simplify the model's output and make it easier to process in bulk.
What was the outcome of running the question through a loop 100 times?
-Llama 3 answered 'dining room' 98 times and 'kitchen' twice, indicating a high level of accuracy in its responses when given the same question multiple times.
What did the experimenter conclude about the importance of crafting the correct prompt for Llama 3?
-The experimenter concluded that the way a question is phrased or prompted to Llama 3 is crucial, as it significantly influences the model's responses and the consistency of the answers received.
How did the experimenter handle the situation when Llama 3 provided too much information in its response?
-The experimenter instructed Llama 3 to provide only a one-letter answer (A or B) to simplify the response and make it easier to analyze in bulk.
What was the experimenter's strategy to ensure a consistent answer from Llama 3?
-The experimenter used a loop to ask the question multiple times and then tallied the responses to determine the consistency of Llama 3's answers.
What was the final verdict on Llama 3's performance in this experiment?
-The final verdict was that Llama 3 provided the correct answer 98% of the time, which the experimenter considered to be a high level of accuracy and a positive outcome for the experiment.
What is the experimenter's advice for using large language models like Llama 3?
-The experimenter advises that it is important to conduct thorough testing, craft the correct prompt, and be cautious not to rely solely on the system when the correct answer is not already known.
Outlines
🤖 Automating Queries with Python and LLMs
The speaker discusses their previous video where they posed a question to two different versions of Microsoft's LLM, 'Llama 3' and '53'. They reflect on the results and express a desire to ask the same question to 'Llama 3' 100 times to see how the answers may vary. They explain their plan to use Python with the 'olama' package to interact with the LLM locally, which includes installing the package, defining a response variable, and setting up a message with the question. They also detail the process of asking the question and receiving a response, and how they plan to automate this process to test the model's consistency.
🔁 Experimenting with Model Responses
The speaker continues to experiment with the LLM by asking the same question multiple times and observing the variability in the responses. They note that the model seems to struggle with providing a one-letter answer (A or B) and instead gives more detailed responses. After several attempts to refine the prompt for a concise answer, the speaker decides to run a loop to ask the question 10 times and observes that the model predominantly provides the correct answer, 'dining room'. They express curiosity about the nature of language models and the importance of crafting the right prompts to get accurate responses.
📈 Analyzing Model Consistency
The speaker creates a method to store and analyze the responses from the LLM. They establish a list called 'answers' and append each response to this list. They then run the question 20 times and observe that the model consistently provides the correct answer, 'dining room'. The speaker further refines the process by running the question 100 times and uses conditional statements to tally the occurrences of each answer. The results show that the model answered correctly 98% of the time and incorrectly 2% of the time, leading the speaker to reconsider previous criticisms of the model's accuracy.
🔍 Reflecting on the Testing Process
In the final paragraph, the speaker reflects on the testing process and the reliability of the LLM. They express concern about the potential pitfalls of relying on an AI system when the correct answer is not already known. The speaker emphasizes the importance of crafting the correct prompt and conducting extensive testing to ensure the model's responses are reliable. They conclude by encouraging viewers to subscribe for more content on Python, problem-solving, and working with large language models, and they look forward to discussing further in the next video.
Mindmap
Keywords
💡Llama 3
💡Python
💡oLlama
💡Web Server
💡Language Models
💡Prompting
💡Multiple Choice Response
💡Local Machine
💡Data and Language
💡Consistency in AI Responses
💡Automating Questions
Highlights
The video tests Llama 3's responses to a question by asking it 100 times to see variability.
Using the oLlama package in Python to interact with large language models locally.
The process of installing oLlama for Python interaction demonstrated.
Importing oLlama and using it to define a response variable for model interaction.
Crafting a question for Llama 3 regarding a scenario with a cake and a plate.
Automating the process to get multiple responses from Llama 3.
Tweaking the prompt to receive a multiple-choice style response.
Observing inconsistencies in Llama 3's answers even with the same question.
The importance of correct prompting to get reliable answers from language models.
Using a loop to ask the question 10 times to check consistency.
The challenge of getting Llama 3 to provide a single letter answer reliably.
Creating a method to save and analyze multiple responses for accuracy.
Conducting 100 iterations of the question to gather a comprehensive dataset.
Analysis of 100 responses shows Llama 3 got the correct answer 98% of the time.
The necessity of crafting the correct prompt for reliable outcomes from AI models.
The video concludes that with the right prompt, Llama 3 can provide accurate responses consistently.