{"id":593556,"date":"2023-01-03T05:48:51","date_gmt":"2023-01-03T11:48:51","guid":{"rendered":"https:\/\/news.sellorbuyhomefast.com\/index.php\/2023\/01\/03\/theres-now-an-open-source-alternative-to-chatgpt-but-good-luck-running-it\/"},"modified":"2023-01-03T05:48:51","modified_gmt":"2023-01-03T11:48:51","slug":"theres-now-an-open-source-alternative-to-chatgpt-but-good-luck-running-it","status":"publish","type":"post","link":"https:\/\/newsycanuse.com\/index.php\/2023\/01\/03\/theres-now-an-open-source-alternative-to-chatgpt-but-good-luck-running-it\/","title":{"rendered":"There\u2019s now an open source alternative to ChatGPT, but good luck running it"},"content":{"rendered":"
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The first open source equivalent of OpenAI\u2019s ChatGPT<\/a> has arrived, but good luck running it on your laptop \u2014 or at all.<\/p>\n

This week, Philip Wang, the developer responsible for reverse-engineering closed-sourced AI systems including Meta\u2019s Make-A-Video<\/a>, released PaLM + RLHF, a text-generating model that behaves similarly to ChatGPT. The system combines PaLM<\/a>, a large language model from Google, and a technique called Reinforcement Learning with Human Feedback \u2014 RLHF, for short \u2014 to create a system that can accomplish pretty much any task that ChatGPT can, including drafting emails and suggesting computer code.<\/p>\n

But PaLM + RLHF isn\u2019t pre-trained. That is to say, the system hasn\u2019t been trained on the example data from the web necessary for it to actually work. Downloading PaLM + RLHF won\u2019t magically install a ChatGPT-like experience \u2014 that would require compiling gigabytes of text from which the model can learn and finding hardware beefy enough to handle the training workload.<\/p>\n

Like ChatGPT, PaLM + RLHF is essentially a statistical tool to predict words. When fed an enormous number of examples from training data \u2014 e.g., posts from Reddit, news articles and e-books \u2014 PaLM + RLHF learns how likely words are to occur based on patterns like the semantic context of surrounding text.<\/p>\n

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 \u2014 in PaLM + RLHF\u2019s case, PaLM \u2014 and fine-tuning it on a dataset that includes prompts (e.g., \u201cExplain machine learning to a six-year-old\u201d) paired with what human volunteers expect the model to say (e.g., \u201cMachine learning is a form of AI\u2026\u201d). 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 \u201creward model\u201d that takes the original model\u2019s responses and sorts them in order of preference, filtering for the top answers to a given prompt.<\/p>\n

It\u2019s an expensive process, collecting the training data. And training itself isn\u2019t cheap. PaLM is 540 billion parameters in size, \u201cparameters\u201d referring to the parts of the language model learned from the training data. A 2020 study<\/a> 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<\/a>, which has 176 billion parameters, it took three months using 384 Nvidia A100 GPUs; a single A100 costs thousands of dollars.<\/p>\n

Running a trained model of PaLM + RLHF\u2019s size isn\u2019t trivial, either. Bloom<\/a> requires a dedicated PC with around eight A100 GPUs. Cloud alternatives are pricey, with back-of-the-envelope math finding<\/a> the cost of running OpenAI\u2019s text-generating GPT-3<\/a> \u2014 which has around 175 billion parameters \u2014 on a single Amazon Web Services instance to be around $87,000 per year.<\/p>\n

Sebastian Raschka, an AI researcher, points out in a LinkedIn post<\/a> about PaLM + RLHF that scaling up the necessary dev workflows could prove to be a challenge as well. \u201cEven 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,\u201d he said. \u201cIt\u2019s obviously possible, but it\u2019s a big effort at the moment (of course, we are developing frameworks to make that simpler, but it\u2019s still not trivial, yet).\u201d<\/p>\n

That\u2019s all to say that PaLM + RLHF isn\u2019t going to replace ChatGPT today \u2014 unless a well-funded venture (or person) goes to the trouble of training and making it available publicly.<\/p>\n

In better news, several other efforts to replicate ChatGPT are progressing at a fast clip, including one led by a research group called CarperAI<\/a>. 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.<\/p>\n

LAION, the nonprofit that supplied the initial dataset used to train Stable Diffusion<\/a>, is also spearheading<\/a> a project to replicate ChatGPT using the newest machine learning techniques. Ambitiously, LAION aims to build an \u201cassistant of the future\u201d \u2014 one that not only writes emails and cover letters but \u201cdoes meaningful work, uses APIs, dynamically researches information and much more.\u201d It\u2019s in the early stages. But a GitHub page<\/a> with resources for the project went live a few weeks ago.<\/p>\n<\/p><\/div>\n

Read More<\/a>
\n Kyle Wiggers<\/p>\n","protected":false},"excerpt":{"rendered":"

The first open source equivalent of OpenAI\u2019s ChatGPT has arrived, but good luck running it on your laptop \u2014 or at all. This week, Philip Wang, the developer responsible for reverse-engineering closed-sourced AI systems including Meta\u2019s Make-A-Video, released PaLM + RLHF, a text-generating model that behaves similarly to ChatGPT. The system combines PaLM, a large<\/p>\n","protected":false},"author":1,"featured_media":593557,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3278,46,63],"tags":[],"class_list":{"0":"post-593556","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-source","8":"category-technology","9":"category-theres"},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/593556","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/comments?post=593556"}],"version-history":[{"count":0,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/593556\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media\/593557"}],"wp:attachment":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media?parent=593556"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/categories?post=593556"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/tags?post=593556"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}