There’s now an open source alternative to ChatGPT, but good luck running it

The first open source equivalent of OpenAI’s ChatGPT has arrived, but good luck running it on your laptop — or at all.

This week, Philip Wang, the developer responsible for reverse-engineering closed-sourced AI systems including Meta’s Make-A-Video, released PaLM + RLHF, a text-generating model that behaves similarly to ChatGPT. The system combines PaLM, a large language model from Google, and a technique called Reinforcement Learning with Human Feedback — RLHF, for short — to create a system that can accomplish pretty much any task that ChatGPT can, including drafting emails and suggesting computer code.

But PaLM + RLHF isn’t pre-trained. That is to say, the system hasn’t been trained on the example data from the web necessary for it to actually work. Downloading PaLM + RLHF won’t magically install a ChatGPT-like experience — that would require compiling gigabytes of text from which the model can learn and finding hardware beefy enough to handle the training workload.

Like ChatGPT, PaLM + RLHF is essentially a statistical tool to predict words. When fed an enormous number of examples from training data — e.g., posts from Reddit, news articles and e-books — PaLM + RLHF learns how likely words are to occur based on patterns like the semantic context of surrounding text.

ChatGPT and PaLM + RLHF share a special sauce in Reinforcement Learning with Human Feedback, a technique that aims to better align language models with what users wish them to accomplish. RLHF involves training a language model — in PaLM + RLHF’s case, PaLM — and fine-tuning it on a dataset that includes prompts (e.g., “Explain machine learning to a six-year-old”) paired with what human volunteers expect the model to say (e.g., “Machine learning is a form of AI…”). The aforementioned prompts are then fed to the fine-tuned model, which generates several responses, and the volunteers rank all the responses from best to worst. Finally, the rankings are used to train a “reward model” that takes the original model’s responses and sorts them in order of preference, filtering for the top answers to a given prompt.

It’s an expensive process, collecting the training data. And training itself isn’t cheap. PaLM is 540 billion parameters in size, “parameters” referring to the parts of the language model learned from the training data. A 2020 study pegged the expenses for developing a text-generating model with only 1.5 billion parameters at as much as $1.6 million. And to train the open source model Bloom, which has 176 billion parameters, it took three months using 384 Nvidia A100 GPUs; a single A100 costs thousands of dollars.

Running a trained model of PaLM + RLHF’s size isn’t trivial, either. Bloom requires a dedicated PC with around eight A100 GPUs. Cloud alternatives are pricey, with back-of-the-envelope math finding the cost of running OpenAI’s text-generating GPT-3 — which has around 175 billion parameters — on a single Amazon Web Services instance to be around $87,000 per year.

Sebastian Raschka, an AI researcher, points out in a LinkedIn post about PaLM + RLHF that scaling up the necessary dev workflows could prove to be a challenge as well. “Even if someone provides you with 500 GPUs to train this model, you still need to have to deal with infrastructure and have a software framework that can handle that,” he said. “It’s obviously possible, but it’s a big effort at the moment (of course, we are developing frameworks to make that simpler, but it’s still not trivial, yet).”

That’s all to say that PaLM + RLHF isn’t going to replace ChatGPT today — unless a well-funded venture (or person) goes to the trouble of training and making it available publicly.

In better news, several other efforts to replicate ChatGPT are progressing at a fast clip, including one led by a research group called CarperAI. In partnership with the open AI research organization EleutherAI and startups Scale AI and Hugging Face, CarperAI plans to release the first ready-to-run, ChatGPT-like AI model trained with human feedback.

LAION, the nonprofit that supplied the initial dataset used to train Stable Diffusion, is also spearheading a project to replicate ChatGPT using the newest machine learning techniques. Ambitiously, LAION aims to build an “assistant of the future” — one that not only writes emails and cover letters but “does meaningful work, uses APIs, dynamically researches information and much more.” It’s in the early stages. But a GitHub page with resources for the project went live a few weeks ago.