The video introduces Smaug 70b, a 70 billion parameter fine-tuned model, as superior to GPT 4 Turbo, showcasing its performance against a smaller quantized version. The comparison reveals that while the larger model excels in some tasks, the smaller locally-run quantized model shows promising performance in specific scenarios, highlighting the potential of efficient AI applications.
In the video, Abacus AI introduces Smaug 70b as a 70 billion parameter fine-tuned version of Llama 3, claiming it to be better than GPT 4 Turbo. The model is tested against a smaller 7 billion parameter quantized version locally. The benchmarks show that Smaug 70b outperforms Llama 3 and GPT 4 Turbo across various tests, including Mt bench and arena hard scores. The model is successfully run on abacus.com and a quantized version is tested locally on LM Studio.
Different tasks are assigned to both the larger and smaller versions of the model to test their capabilities. They are tasked with simple tasks like outputting numbers, creating a game of Snake, solving math problems, and answering logic puzzles. The larger model struggles with some tasks, such as answering a tricky logical puzzle about killers in a room, while the smaller model performs better in certain scenarios like calculating drying time for shirts and solving word problems.
The video provides comparisons between the larger and smaller models’ performances in various tasks. The larger model fails in tasks like word count and reasoning puzzles, while the smaller model excels in tasks like predicting where a ball is placed and generating sentences ending with a specific word. The results suggest that the smaller, locally run quantized model performs comparably or better than the larger model in some scenarios.
The video includes a segment sponsored by Tune AI, showcasing how developers can use the platform to manage multiple language models and monitor API responses effectively. The video concludes with a summary of the model’s performance in different tasks and highlights the potential of running high-quality models locally. The overall assessment suggests that the smaller quantized model shows promising performance and offers a viable option for running complex models efficiently.
In conclusion, the video demonstrates the capabilities of Smaug 70b, a 70 billion parameter model, compared to a smaller quantized version in various tasks. The benchmarks show that the larger model outperforms in some areas but struggles in others, while the smaller model performs well in certain scenarios. The comparison highlights the potential of locally running quantized models for efficient and effective AI applications.