Phi-3-mini vs Llama 3 Showdown: Testing the real world applications of small LLMs

Summary

This video compares Meta's Llama 3 (8 billion parameters) and Microsoft's F3 Mini (3.8 billion parameters) language models across various tasks like recall, pattern recognition, and SQL coding. The speaker evaluates both models' performance on a range of tests, revealing that Llama 3 outperforms F3 Mini in recall accuracy, pattern-solving, and SQL generation. While F3 Mini is a solid choice for hardware-constrained scenarios, Llama 3 offers superior performance overall, especially for tasks requiring reasoning and complex context handling. The conclusion recommends Llama 3 for users with sufficient hardware, as it delivers more reliable results.

Takeaways

• 😀 The world of large language models is evolving rapidly, with Meta releasing Llama 3 and Microsoft unveiling F3 Mini, each making bold claims about performance.
• 😀 Microsoft claims that F3 Mini (3.8B parameters) outperforms the Mistral 8.7B mixture of experts model and even GPT-3.5 in various benchmarks.
• 😀 A major claim by Microsoft is that F3 Mini (3.8B) is comparable to Llama 3's 38B model, but this claim is put to the test in the video.
• 😀 The speaker compares the F3 Mini and Llama 3 8B models using three types of questions: recall, pattern recognition, and coding awareness.
• 😀 For the recall test, the Llama 3 model correctly identifies the dish of the day, while F3 Mini fails to mention it in its response.
• 😀 In the pattern recognition test, both models struggle to fully solve a numerical pattern, but Llama 3 (8B) does slightly better in identifying squares and cubes in the sequence.
• 😀 In the SQL query test, Llama 3 provides a workable, albeit imperfect, SQL query, whereas F3 Mini fails to produce a correct SQL query and instead generates a fake example.
• 😀 Llama 3 (8B) outperforms F3 Mini in terms of reasoning ability and recall accuracy, especially when tasked with coding or pattern recognition.
• 😀 The speaker uses a 100 GPU for testing and mentions that Llama 3 performs better when using a quantized form on sufficient hardware resources.
• 😀 Despite F3 Mini's smaller size and lower computational requirements, it is recommended only in cases of hardware constraints, with Llama 3 (8B) being the better choice for most users.

Q & A

What are the two language models being compared in the video?

The two language models compared in the video are Meta’s Llama 3 (8 billion parameters) and Microsoft's F3 Mini (3.8 billion parameters).

What are the main benchmarks the models are being tested on?

The models are being tested on three main tasks: recall (identifying the dish of the day), pattern recognition (solving a number sequence puzzle), and coding (writing a SQL query).

Which model performed better on the recall task (identifying the dish of the day)?

Llama 3 performed better on the recall task, correctly identifying the dish of the day as 'Pai', while F3 Mini failed to mention the dish of the day.

What was the issue with Llama 3’s answer in the pattern recognition task?

Llama 3 correctly identified that the sequence involved squaring numbers but made an error in the final answer, returning 36 instead of the correct 49.

How did F3 Mini handle the pattern recognition task?

F3 Mini identified the cube pattern but failed to recognize the mix of squaring and cubing in the sequence, giving an incomplete answer.

Did F3 Mini provide a functional solution to the SQL query problem?

No, F3 Mini did not provide a functional SQL query. Instead, it generated a fake example, which didn't solve the problem directly.

How did Llama 3 perform on the SQL query task?

Llama 3 generated a reasonable SQL query to return the name of the manager with five or more reporting employees, although it had some errors in the query logic.

What hardware was used to run the models in the video?

The models were run on an A100 GPU, and the script mentions that no quantization was applied to the models for this comparison.

What is the key conclusion of the video regarding F3 Mini and Llama 3?

The key conclusion is that while F3 Mini is a good model, it doesn’t outperform Llama 3. If hardware permits, Llama 3 (in a quantized form) is recommended for better overall performance in recall, pattern recognition, and coding tasks.

Under what circumstances would F3 Mini be a good choice over Llama 3?

F3 Mini would be a good choice if you're constrained by hardware requirements, as it is smaller and more lightweight compared to Llama 3. However, for tasks requiring better performance, Llama 3 is still the preferred option.