Zephyr 7B, a 7-billion-parameter model from Hugging Face, beats ChatGPT on two major benchmarks: MTBench (0.82 vs 0.67) and Alpaca Eval (0.77 vs 0.63). Trained on public data and synthetic examples using Direct Preference Optimization, it shows what smaller models can do with the right training method.
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7 billion parameters
Hugging Face H4 team
Direct Preference Optimization (DPO)
0.82 out of 1
0.67 out of 1
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A new language model, Zephyr 7B, has arrived and it's doing something remarkable. It's actually performing better than ChatGPT in some key tests, which is pretty impressive. So it's a model with 7 billion parameters designed to be a helpful assistant. Zephyr 7B was developed by the Hugging Face H4 team, who are known for their amazing work on Transformers and other open source AI projects. They used a mix of publicly available and synthetic data sets to train Zephyr 7B. And they used a novel technique called Direct Preference Optimization, or DPO for short. DPO is a way of fine tuning language models based on human preferences rather than labels or rewards. Basically, they used another powerful language model like GPT4 or Claude to rank the outputs of Zephyr 7B and compare them with other models. Then they used a reinforcement learning algorithm to optimize Zephyr 7B based on these rankings. This training method is pretty innovative. It doesn't need human annotations or feedback, which are hard and costly to gather. It also lets the model learn from a wider range of data, not just limited to specific tasks. And the great thing is, it's proving to be very effective. So how effective is it? The Hugging Face H4 team shared some results in their paper. They tested Zephyr 7B on MT bench and Alpaca Evil benchmarks. MT bench checks how well the chatbot can follow instructions and converse, while Alpaca Evil uses human preference to rate chatbot responses. On both benchmarks, Zephyr 7B beat ChatGPT by a significant margin. On MTBench, Zephyr 7B scored 0.82 out of 1, while ChatGPT scored 0.67. On Alpaca Evil, Zephyr 7B scored0.77 out of 1, while ChatGpt scored 0.63. These are huge differences, especially considering that ChatGPT is already a very impressive model that was trained on millions of real world dialog. Zephyr 7B is better than ChatGPT for a few reasons. First, it's bigger, which means Zephyr can hold more information and understand more complex ideas. Also, it's built differently. The way Zephyr 7B learns is really important too. It uses direct Preference optimization to get better based on what humans like, while ChatGPT improves using supervised fine tuning, which depends on specific labels or rewards. DPO lets Zephyr learn from a wider variety of examples and focus on what people actually want, instead of just trying to meet a set goal or reward. Because of this, Zephyr7b can give more useful, clear and relevant answers than ChatGPT in many situations. For instance, if you ask it to write about cats, Zephyr 7B can create a detailed, well organized essay about different things related to cats. ChatGPT might just write something more basic and repetitive about how cute cats are when explaining complex topics like quantum mechanics. Zephyr 7B can give a straightforward and easy to understand explanation with simple examples. ChatGPT might end up giving a more complicated and confusing answer with lots of technical terms. In casual conversations, Zephyr 7B comes across as friendly and natural, showing humor and empathy. ChatGPT, however, can seem more stiff and sometimes gives odd or unrelated answers. These examples just scratch the surface of how Zephyr 7B is better in different ways. You can find more examples on the Hugging Face website where you can Also test out Zephyr 7B yourself with your own questions. Now I believe we're entering a new phase where language models are more than just text generators. However, Zephyr 7B isn't without its flaws. It has its own set of challenges, like dealing with biases and inconsistencies that might affect how well it works and how much you can rely on it. It also struggles a bit when trying to scale up, which could hold back its performance and effectiveness. Plus, it needs more data and feedback to get better at handling different situations and being more reliable. But these challenges aren't impossible to overcome, they're actually chances for more research and improvement. I'm confident that the team at hugging face H4 and the broader AI community will are going to keep pushing to enhance Zephyr 7B and other language models. That's all for today's video. I hope you found it interesting and learned something if you liked it. Don't forget to hit the thumbs up and subscribe for more content about AI and chatbots. I'd love to hear what you think about Zephyr 7B and how you might use it in your projects or everyday tasks. Thanks for tuning in and I'll catch you in the next one.
