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میخوام برم توی خیابون داد بزنم هر کسی میبینم بگیرم تکونش بدم بگم که جی پی تی ۴ ریلیز شد!

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خدای من!!!!!

We’ve spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.

In a casual conversation, the distinction between GPT-3.5 and GPT-4 can be subtle. The difference comes out when the complexity of the task reaches a sufficient threshold—GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5.

Over a range of domains—including documents with text and photographs, diagrams, or screenshots—GPT-4 exhibits similar capabilities as it does on text-only inputs. Furthermore, it can be augmented with test-time techniques that were developed for text-only language models, including few-shot and chain-of-thought prompting. Image inputs are still a research preview and not publicly available.

GPT-4 generally lacks knowledge of events that have occurred after the vast majority of its data cuts off (September 2021), and does not learn from its experience.

GPT-4 incorporates an additional safety reward signal during RLHF training to reduce harmful outputs (as defined by our usage guidelines) by training the model to refuse requests for such content. The reward is provided by a GPT-4 zero-shot classifier judging safety boundaries and completion style on safety-related prompts.

We will soon share more of our thinking on the potential social and economic impacts of GPT-4 and other AI systems.

A large focus of the GPT-4 project has been building a deep learning stack that scales predictably. The primary reason is that, for very large training runs like GPT-4, it is not feasible to do extensive model-specific tuning. We developed infrastructure and optimization that have very predictable behavior across multiple scales. To verify this scalability, we accurately predicted in advance GPT-4’s final loss on our internal codebase (not part of the training set) by extrapolating from models trained using the same methodology but using 10,000x less compute

We’re open-sourcing OpenAI Evals, our software framework for creating and running benchmarks for evaluating models like GPT-4, while inspecting their performance sample by sample.

we have also included the templates that have been most useful internally (including a template for “model-graded evals” — we’ve found that GPT-4 is surprisingly capable of checking its own work)

ChatGPT Plus subscribers will get GPT-4 access on chat.openai.com with a usage cap.

Depending on the traffic patterns we see, we may introduce a new subnoscription level for higher-volume GPT-4 usage; we also hope at some point to offer some amount of free GPT-4 queries so those without a subnoscription can try it too.

این مدل برخلاف GPT-3 که باهاش ChatGPT رو ساختن فقط متنی نیست و قابلیت چند رسانه‌ای یا Multi modal داره!

تعداد پارامترهای اون 500 برابر GPT-3 هستش یعنی 500 * 175 میلیارد پارامتر

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