Liquid AI's New Model: Performance and Testing Insights

Liquid AI's new generative model excels in benchmarks but struggles with practical tasks.

### Liquid AI's New Model: A Deep Dive into Performance and Testing
* 🤖 Liquid AI has introduced a new architecture that deviates from the traditional **Transformers model**, marking a **significant shift in generative AI technology**.
* 📊 The Liquid foundation models consist of three sizes: **1 billion**, **3 billion**, and **40 billion parameters**, with the latter being a **mixture of experts model** that showcases **exceptional performance**.
* 🏆 Benchmarks reveal that the **40B model outperforms competitors**, including Llama 3.1 and Mixture Jum Jamba Quen 57B, with **12 billion active parameters utilized at any time**.
* 💾 One standout feature of these models is their **memory efficiency**, allowing the 40B model to **handle up to a million tokens** before memory usage **significantly increases**.
* 🔍 Despite the impressive specs, initial tests on tasks like coding and logic puzzles yielded mixed results, with the model **failing to correctly execute a Tetris game** and some **logical reasoning problems**.
* ❌ The model struggled with **basic counting and reasoning tasks**, raising questions about its **training and effectiveness** compared to benchmarks.
* 💔 Overall, while the benchmarks suggest **high potential**, the practical performance during testing was **disappointing**, leaving **room for improvement** in future iterations.
* 👍 The exploration of non-Transformer models continues, with hopes that one will eventually **meet or exceed expectations** in real-world applications.

New “Liquid” Model - Benchmarks Are Useless

Artificial Intelligence (AI), Open Source, Generative Art, AI Art, Futurism, ChatGPT, Large Language Models (LLM), Machine Learning, Technology, Coding, Tutorials, AI News, and more

** Exclusive Pinecone Partner **

Matthew Berman

Liquid AI has launched a new series of generative AI models with a unique architecture, different from Transformers.

The models come in three sizes: 1 billion, 3 billion, and 40 billion, with the 40B model being a mixture of experts.

The Liquid foundation models perform exceptionally well, with the 1.

1B models optimized for resource-constrained environments, while the 40B model is designed for more complex tasks.

The Performance of Liquid Foundational Models

The 3B and 40 billion parameter models excelled in MMLU Pro but didn't consistently beat other models.

Liquid foundational models stand out with their memory efficiency, scaling up to a million tokens in output length before increasing significantly.

They claim to offer powerful context lengths, optimizing their pre- and post-training pipelines to excel in knowledge capacity, multi-step reasoning, long context recall, inference efficiency, and training efficiency.

The Tetris game attempt failed due to incomplete code.

The envelope problem was solved by rotating it, resulting in a pass.

The response to the word count prompt was incorrect, leading to a fail, while the killer's problem had the right answer but flawed logic.

° and walking, can lead to passing the original starting point, but the answer varies.

The attempt to generate 10 sentences ending with the word "Apple" was unsuccessful, leading to frustration with the model's performance.

Evaluating Model Performance on Questions

The model struggled with basic questions, such as counting letters and comparing numbers, leading to several failures.

It provided a nuanced answer to a morality question, but did not give a clear yes or no, which was considered a failure.

Overall, the model's performance on the benchmark questions was disappointing, leaving the evaluator hopeful for a better non-transformer model in the future.