New “Liquid” Model - Benchmarks Are Useless

1. The Liquid Model introduces a new architecture distinct from Transformers.

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Liquid AI's new model is not based on the traditional Transformers architecture, marking a significant shift in generative AI design.

• This model family includes three sizes: 1 billion, 3 billion, and 40 billion parameters.
• Liquid foundation models are designed to excel in various benchmarks.
• The architecture aims to improve memory efficiency and performance.

2. Benchmark performance varies across different model sizes.

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While the Liquid Models perform well in benchmarks, results differ by model size, with the 3B and 40B models showing notable strengths.

• The 1 billion model excels in specific benchmarks, winning against competitors.
• The 40 billion model is particularly effective in multi-step reasoning tasks.
• However, not all benchmarks are won, indicating areas for improvement.

3. Liquid Models demonstrate superior memory efficiency.

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These models maintain low memory footprints even with large output lengths, outperforming competitors in memory usage.

• The 40 billion parameter model can handle up to a million tokens before memory usage spikes.
• In contrast, other models show significant memory increases at lower token counts.
• This efficiency is crucial for deployment in resource-constrained environments.

4. Testing reveals mixed results for the Liquid Model's capabilities.

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Initial tests on various tasks show that the model struggles with certain logic and reasoning challenges.

• The model failed to generate correct code for a simple game.
• It performed inconsistently on logic puzzles and basic math questions.
• These results suggest that while benchmarks are promising, practical applications may vary.