Mixture of Agents (MoA) BEATS GPT4o With Open-Source (Fully Tested)
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The expert discusses the effectiveness of multiple open-source models collaborating for logic and reasoning tasks, highlighting a recent breakthrough in surpassing GPT-3.
Collaborative Open-Source Models
- Multiple open-source models working together excel at logic and reasoning.
- Mixture of Agents outperformed GPT-3 by leveraging collaboration among large language models.
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The expert prepares to evaluate the collaborative model using the LLM rubric, introducing four key models for testing.
Evaluation Setup
- Loading the vanilla version with Together AI for evaluation.
- Four models selected for testing: Quen 272b instruct, Quen 1.5 72b chat, MixL8 time 22b instruct, and DBRX instruct.
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Configuration settings are adjusted before initiating tests to ensure optimal performance.
Configuration Settings
- Customizing temperature to default value of 7 and Max tokens to enhance performance.
- Initial prompt involves writing a Python script querying all four models simultaneously.
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Results from the collaborative model are analyzed after running Python scripts for various tasks.
Analysis of Results
- Aggregated answer obtained from multiple models with Quen 272b as the aggregator.
- Suggestions provided for enhancing script versatility beyond initial requirements.
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The expert proceeds to test game development capabilities using Python within the collaborative model framework.
Game Development Testing
- Attempting to create a Snake game in Python utilizing Turtle Graphics Library.
- Challenges faced during game creation due to speed limitations when querying multiple models simultaneously.
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In this section, the speaker discusses a scenario involving hotel charges and taxes, prompting the audience to determine the correct formula for calculating the total cost.
Hotel Charges Calculation
- The hotel room rate is $99.95 per night plus an 8% tax and a one-time untaxed fee of $5.
- The correct formula for calculating the total cost is 1.08 * 99.95x + 5.
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This part involves a question about determining the number of words in a response and explores how multiple models can work together to solve complex problems effectively.
Word Count Analysis
- The response contains 626 words, not matching the expected count of 761 words.
- Multiple models working together can enhance problem-solving capabilities.
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The discussion shifts towards analyzing an architecture where agents output responses to solve problems collectively rather than individually.
Agent Architecture Analysis
- Each agent outputs its estimation in layers, culminating in one final model's judgment.
- The effectiveness of this architecture in determining word count is questioned due to aggregation challenges.
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A riddle involving killers in a room is presented, requiring logical reasoning to deduce the number of remaining killers after an event occurs.
Killer Riddle Solution
- Initially, three killers are present in the room.
- After an event where one killer is killed by another person, there remain three killers due to new criteria fulfillment.
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A challenging logic and reasoning problem regarding a marble's location through various actions is explored with potential solutions using multiple models' collaboration.
Marble Location Puzzle
- Depending on factors like size and glass opening, the marble's location varies.
- Without specific details, definitively determining if the marble is in the microwave remains inconclusive but highlights collaborative model effectiveness.
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An assessment task involving generating sentences ending with "Apple" tests model performance and correction capabilities within layered architectures.
Sentence Generation Evaluation
- Despite initial failure, subsequent attempts showcase correction abilities within layered models.
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In this section, the speaker discusses the concept of man-hours and considerations when multiple people are involved in completing a task.
Understanding Time Efficiency
- When multiple people are involved in a task, the concept of man-hours comes into play to determine how long it would take for them to complete it.
- Practical considerations such as proportional reduction in time, size of the hole, tools and resources, coordination, communication efficiency, and productivity should be taken into account.