Yi-1.5: The True Apache 2.0 Competitor to LLAMA-3

The Yi-1.5 series models, developed by 01-AI, have just been released and are making waves with their impressive performance and open-source Apache 2.0 licensing. These models range in size from 6 billion to 34 billion parameters and have been trained on up to 4.1 trillion tokens. The video provides a comprehensive overview of the Yi models, including their specifications, performance benchmarks, and practical testing.

The video begins by introducing the upgraded Yi model family, highlighting their ability to extend the context window of an open LLM to 200,000 tokens. Three different models are discussed: 6 billion, 9 billion, and 34 billion parameters, all fine-tuned on 3 million samples. Despite a smaller context window of 4,000 tokens for modern LLMs, the models are noted for their strong performance in coding, math, reasoning, and instruction following capabilities.

The host tests the models using various prompts to evaluate their capabilities. The 9 billion parameter model, running on a local machine, is used for most tests. The model’s ability to handle uncensored content, reasoning, deduction, and mathematical problems is demonstrated. For example, it successfully reasons through complex scenarios and performs mathematical calculations accurately.

The video also explores the model’s contextual understanding and ability to retrieve information, making it suitable for Retrieval-Augmented Generation (RAG) tasks. The model’s programming and coding proficiency is tested by providing Python and HTML code snippets, which it handles effectively, although with some limitations in more complex tasks.

The host concludes by recommending the Yi-1.5 models for LLM-based applications, particularly for their commercial usability under the Apache 2.0 license. The upcoming release of the Yi large model, expected to compete with GPT-4, is also mentioned as an exciting prospect.

Overall, this video is a valuable resource for developers and AI enthusiasts looking to explore and integrate high-performance, open-source language models into their projects.