According to Bindu, the CEO of Abacus AI, the Smaug 70b model is significantly better than the previous best open-source model, LLaMA 37b. Smaug 70b outperforms LLaMA 37b and GPT-4 Turbo across various benchmarks, including MT bench and Arena hard scores.

The Smaug 70b model scored 56.7 on the MT bench, while LLaMA 37b scored 41.1. This demonstrates the improved reasoning and capability of the Smaug 70b model compared to its predecessor.

To further test the model, the author downloaded a 7 billion parameter quantized version of the Smaug model and ran it locally using LM Studio. The smaller model was able to successfully create a working Snake game, showcasing its versatility and performance.

The author then proceeded to test the larger 70 billion parameter version of the Smaug model on Abacus.com. The model was able to complete various tasks, such as outputting numbers 1 to 100 and solving simple math problems. However, it struggled with more complex tasks, such as creating a Snake game using the Curses library or providing a step-by-step solution to a logic puzzle.

In contrast, the smaller 7 billion parameter quantized model running locally performed better on these more complex tasks, highlighting the potential benefits of using a smaller, optimized model for certain applications.

Overall, the Smaug 70b model demonstrates impressive performance on various benchmarks, outperforming the previous state-of-the-art LLaMA 37b model. However, the author's testing also suggests that the smaller, quantized version of the model may be more suitable for certain use cases, particularly when running locally.

The transcript indicates that the author tested two versions of the Smog model, a 70 billion parameter unquantized version and a 7 billion parameter quantized version, on various tasks. Here is a summary of the key points:

• The author first tested the ability of both models to output numbers 1 to 100 in a Python script, which both models were able to do successfully.
• Next, the author tested the models' ability to create a Snake game in Python. The smaller 7 billion parameter quantized model was able to create a working Snake game on the first try, while the larger 70 billion parameter version had issues and was not able to create a working game.
• The author then tried to get the larger model to create a Snake game using the pygame library, but it was also not successful in this task.
• The author concluded that the smaller quantized model performed better on the Snake game task compared to the larger unquantized version.

Overall, the results suggest that the smaller quantized model was more capable of handling certain programming tasks, such as creating a working Snake game, compared to the larger unquantized version of the Smog model.

The model performed well on a variety of math and word problems, demonstrating its capabilities in quantitative reasoning and problem-solving. Some key highlights:

• The model was able to correctly solve simple arithmetic problems like "25 - 4 * 2 + 3" and provide the step-by-step reasoning.
• For a word problem involving hotel charges, the model identified the correct formula to calculate the total cost, including tax and fees.
• When asked to explain the reasoning for a tricky logic puzzle about killers in a room, the smaller local model provided a more insightful and accurate response compared to the larger cloud-based version.
• The smaller local model also outperformed the larger one on a simple proportionality problem about drying shirts.
• Both models handled basic programming tasks like generating a sequence of numbers and building a simple game of Snake.

Overall, the results demonstrate the model's strong capabilities in mathematical reasoning and problem-solving, with the smaller local version sometimes outperforming the larger cloud-based one. This suggests that high-quality quantitative reasoning can be achieved even with more compact and efficient model deployments.

The marble in the cup scenario is a classic logic puzzle that tests the ability to reason about the physical world and make logical inferences. In this case, the scenario involves a marble being placed in a glass, the glass being turned upside down and placed on a table, and then the glass being picked up and placed in a microwave.

The key to solving this puzzle is to understand that the marble's position relative to the glass does not change throughout the described actions. Even though the glass is turned upside down and moved, the marble remains at the bottom of the glass, which is now facing upwards.

When the glass is placed in the microwave, the marble is still inside the glass, as the actions performed did not cause the marble to leave the glass. This is the correct answer, as the marble remains in the same position within the glass throughout the entire sequence of events.

John, who put the ball in the box before leaving for work, will assume the ball is still in the box.

Mark, who put the ball in the basket while John was away, will assume the ball is in the basket.

Since both John and Mark have different assumptions about the location of the ball, the final location of the ball is ambiguous based on the information provided.

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The larger model was able to generate 10 sentences that all ended with the word "Apple":

1. The crisp, juicy Apple was a delight to eat.
2. John tossed the ripe Apple into the basket.
3. The Apple tree in the backyard was laden with fruit.
4. She sliced the Apple and added it to the salad.
5. The warm Apple pie filled the kitchen with a delightful aroma.
6. The teacher handed out a shiny Red Apple to each student.
7. The caramel-dipped Apple was a popular treat at the county fair.
8. The Apple cider was the perfect accompaniment to the autumn leaves.
9. The Apple blossom petals danced in the gentle breeze.
10. The young boy eagerly took a bite of the Green Apple.

In contrast, the smaller, locally-run model was only able to generate 7 out of 10 sentences ending with the word "Apple".

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To calculate the time it would take 50 people to dig a single 10-ft hole, we can use a proportional approach:

• It takes 1 person 5 hours to dig a 10-ft hole
• Therefore, it would take 50 people 1/50th of the time, which is 6 minutes

The reasoning is as follows:

• If 1 person takes 5 hours, then 50 people would take 1/50th of that time, which is 5 hours / 50 = 0.1 hours = 6 minutes.
• The digging time is inversely proportional to the number of people, so doubling the number of people halves the digging time.

Therefore, it would take 50 people 6 minutes to dig a single 10-ft hole.

The smaller, 7 billion parameter quantized version of the Smog model performed surprisingly well, often matching or even outperforming the larger 70 billion parameter unquantized version. While the larger model excelled at tasks like generating sentences ending in "Apple", the smaller model was able to handle a variety of other challenges, including math problems, logic puzzles, and coding tasks.

This suggests that for many practical applications, the smaller quantized model may be a viable and more efficient alternative to the larger version. The ability to run high-quality language models locally is also a significant advantage, as it allows for greater control, transparency, and potentially faster response times.

Overall, the results of this comparison were quite interesting and highlight the importance of thoroughly testing and evaluating different model configurations to determine the best fit for a given use case. The performance of the smaller Smog model is certainly impressive and worth considering for developers and researchers looking to leverage powerful language AI capabilities.