What is Chat LLaMA ?

March 3, 2023

AI 叫陣 GPT!Meta 公開 LLaMA 大型語言模型


Meet ChatLLaMA: The First Open-Source Implementation of LLaMA Based on Reinforcement Learning from Human Feedback (RLHF)


Meta has recently released LLaMA, a collection of foundational large language models ranging from 7 to 65 billion parameters.

LLaMA is creating a lot of excitement because it is smaller than GPT-3 but has better performance. For example, LLaMA’s 13B architecture outperforms GPT-3 despite being 10 times smaller. This new collection of fundamental models opens the door to faster inference performance and chatGPT-like real-time assistants while being cost-effective and running on a single GPU.

However, LLaMA was not fine-tuned for instruction tasks with a Reinforcement Learning from Human Feedback (RLHF) training process.

The good news is that today Nebuly has introduced ChatLLaMA, the first open-source implementation of LLaMA based on RLHF:

A complete open-source implementation that enables you to build a ChatGPT-style service based on pre-trained LLaMA models.

Compared to the original ChatGPT, the training process and single-GPU inference are much faster and cheaper by taking advantage of the smaller size of LLaMA architectures.

ChatLLaMA has built-in support for DeepSpeed ZERO to speed up the fine-tuning process.

The library also supports all LLaMA model architectures (7B, 13B, 33B, 65B), so that you can fine-tune the model according to your preferences for training time and inference performance.

If you like the project, please consider leaving a star on the GitHub repository


Get started with ChatLLaMA

ChatLLaMA allows you to easily train LLaMA-based architectures in a similar way to ChatGPT using RLHF. For example, below is the code to start the training in the case of ChatLLaMA 7B.

from chatllama.rlhf.trainer import RLTrainer from chatllama.rlhf.config import Config path = “path_to_config_file.yaml” config = Config(path=path) trainer = RLTrainer(config.trainer) trainer.distillate() trainer.train() trainer.training_stats.plot()

Note that you should provide Meta’s original weights and your custom dataset before starting the fine-tuning process. Alternatively, you can generate your own dataset using LangChain’s agents.

python generate_dataset.py

Call for open-source contributions

Nebuly has open-sourced the complete code to replicate the ChatLLaMA implementation, opening up the possibility for every user to fine-tune their own personalized ChatLLaMA assistants. The library can be further extended with the following additions:

Checkpoints with fine-tuned weights

Optimization techniques for faster inference

Support for packaging the model into an efficient deployment framework

All developers are invited to join Nebuly’s efforts toward more efficient and open ChatGPT-like assistants.

You can participate in the following ways:

Submit an issue or PR on GitHub Join their Discord group to chat

Note: Thanks to Nebuly’s team for the thought leadership/ Educational article above.

Mark Zuckerberg responds to the ChatGPT A.I. race with a new offering from Meta: Meet LLaMA


Meta Platforms Inc. introduced a research tool for building artificial intelligence-based chatbots and other products, seeking to create a buzz for its own technology in a field lately focused on internet rivals Google and Microsoft Corp.

The tool, LLaMA, is Meta’s latest entry in the realm of large language models, which “have shown a lot of promise in generating text, having conversations, summarizing written material and more complicated tasks like solving math theorems or predicting protein structures,” Chief Executive Officer Mark Zuckerberg said in an Instagram post on Friday.

For now LLaMA isn’t in use in Meta’s products, which include social networks Facebook and Instagram, according to a spokesperson. The company plans to make the technology available to AI researchers.

“Meta is committed to this open model of research,” Zuckerberg wrote.

Large language models are massive AI systems that suck up enormous volumes of digital text — from news articles, social media posts or other internet sources — and use that written material to train software that predicts and generates content on its own when given a prompt or query. The models can be used for tasks like writing essays, composing tweets, generating chatbot conversations and suggesting computer programming code.

The technology has become popular, and controversial, in recent months as more companies have started to build them and introduce tests of products based on the models, spotlighting a new area of competition among tech giants. Microsoft is investing billions in OpenAI, the maker of GPT-3, the large language model that runs the ChatGPT chatbot. The software maker this month unveiled a test version of its Bing search engine running on OpenAI’s chat technology, which raised immediate concerns over its sometimes-inappropriate responses.

Alphabet Inc.’s Google has a model called LaMDA, or Language Model for Dialogue Applications. The internet search and advertising leader is testing a chat-based, AI-powered search product called Bard, which also still has some glitches.

Meta previously launched a large language model called OPT-175B, but LLaMA is a newer and more advanced system. Another model Meta released late last year, Galactica, was quickly pulled back after researchers discovered it was routinely sharing biased or inaccurate information with people who used it.

Zuckerberg has made AI a top priority inside the company, often talking about its importance to improving Meta’s products on earnings conference calls and in interviews. While LLaMA is not being used in Meta products now, it’s possible that it will be in the future. Meta for now relies on AI for all kinds of functions, including content moderation and ranking material that appears in user feeds.

Making the LLaMA model open-source allows outsiders to see more clearly how the system works, tweak it to their needs and collaborate on related projects. Last year, Big Science and Hugging Face released BLOOM, an open-source LLM that was intended to make this kind of technology more accessible.

Meta Introduces Its Own AI Chat Bot LLaMA


With multiple companies joining in on the AI Chatbot boom with ChatGPT, Google Bard and Bing AI, Meta CEO Mark Zuckerberg has revealed Meta’s attempt to join in on the tech. In a Facebook Post Zuckerberg announced the release of an AI language model called LLaMA. (Large Language Model Meta AI)

For Research Purposes Only

Zuckerberg has said that this AI will only be available to select researchers rather than a tool that anyone can access. The reasoning behind this is that Meta wishes to maintain the integrity and prevent misuse of the AI which we have seen countless times before with public AI options. By using a controlled group of people they can research and eliminate the biases and toxicity problems that the majority of AI have been facing. Only using a small group also requires less computing power as they will test and train the AI on smaller foundation models.

The approach Meta is taking is a wise approach as it is a natural instinct of the internet to cause carnage, especially with AI and getting it to say things it shouldn’t. You can read Meta’s full blog post here.

What do you think of all these new AI chatbots? let us know in the comments

Cuídate, ChatGPT: Meta lanza LLaMA, la nueva app que ‘entra a la pelea’ por la IA


OpenAI’s ChatGPT is causing real harm


City News Bureau of Chicago, a now-defunct news outfit once legendary as a training ground for tough-as-nails, shoe-leather reporters, famously had as its unofficial motto: “If your mother says she loves you, check it out.” Thanks to the advent of ChatGPT, the new Bing Search, Bard, and a host of copycat search chatbots based on large language models, we are all going to have to start living by City News’ old shibboleth.

Researchers already knew that large language models were imperfect engines for search queries, or any fact-based request really, because of their tendency to make stuff up (a phenomenon A.I. researchers call “hallucination”). But the world’s largest technology companies have decided that the appeal of dialogue as a user interface—and the ability of these large language models to perform a vast array of natural language-based tasks, from translation to summarization, along with the potential to couple these models with access to other software tools that will enable them to perform tasks (whether it is running a search or booking you theater tickets)—trumps the potential downsides of inaccuracy and misinformation.

Except, of course, there can be real victims when these systems hallucinate—or even when they don’t, but merely pick up something that is factually wrong from their training data. Stack Overflow had to ban users from submitting answers to coding questions that were produced using ChatGPT after the site was flooded with code that looked plausible but was incorrect. The science fiction magazine Clarkesworld had to stop taking submissions because so many people were submitting stories crafted not by their own creative genius, but by ChatGPT. Now a German company called OpenCage—which offers an application programming interface that does geocoding, converting physical addresses into latitude and longitude coordinates that can be placed on a map—has said it has been dealing with a growing number of disappointed users who have signed up for its service because ChatGPT erroneously recommended its API as a way to look up the location of a mobile phone based solely on the number. ChatGPT even helpfully wrote python code for users allowing them to call on OpenCage’s API for this purpose.

But, as OpenCage was forced to explain in a blog post, this is not a service it offers, nor one that is even feasible using the company’s technology. OpenCage says that ChatGPT seems to have developed this erroneous belief because it picked up on YouTube tutorials in which people also wrongly claimed OpenCage’s API could be used for reverse mobile phone geolocation. But whereas those erroneous YouTube tutorials only convinced a few people to sign up for OpenCage’s API, ChatGPT has driven people to OpenCage in droves. “The key difference is that humans have learned to be skeptical when getting advice from other humans, for example via a video coding tutorial,” OpenCage wrote. “It seems though that we haven’t yet fully internalized this when it comes to AI in general or ChatGPT specifically.” I guess we better start internalizing.

Meanwhile, after a slew of alarming publicity about the dark side of its new, OpenAI-powered Bing chat feature—where the chatbot calls itself Sydney, becomes petulant, and at times even downright hostile and menacing—Microsoft has decided to restrict the length of conversations users can have with Bing chat. But as I, and many others have found, while this arbitrary restriction on the length of a dialogue apparently makes the new Bing chat safer to use, it also makes it a heck of a lot less useful.

For instance, I asked Bing chat about planning a trip to Greece. I was in the process of trying to get it to detail timings and flight options for an itinerary it had suggested when I suddenly hit the “Oops, I think we’ve reached the end of this conversation. Click ‘New topic,’ if you would!”

The length restriction is clearly a kluge that Microsoft has been forced to implement because it didn’t do rigorous enough testing of its new product in the first place. And there are huge outstanding questions about exactly what Prometheus, the name Microsoft has given to the model that powers the new Bing, really is, and what it is really capable of (no one is claiming the new Bing is sentient or self-aware, but there’s been some very bizarre emergent behavior documented with the new Bing, even beyond the Sydney personality, and Microsoft ought to be transparent about what it understands and doesn’t understand about this behavior, rather than simply pretending it doesn’t exist). Microsoft has been cagey in public about how it and OpenAI created this model. No one outside of Microsoft is exactly sure why it is so prone to taking on the petulant Sydney persona, especially when ChatGPT, based on a smaller, less capable large language model, seems so much better behaved—and again, Microsoft is saying very little about what it does know.

(Earlier research from OpenAI had found that it was often the case that smaller models, trained with better quality data, produced results that human users much preferred even though they were less capable when measured on a number of benchmark tests than larger models. That has led some to speculate that Prometheus is OpenAI’s GPT-4, a model believed to be many times more massive than any it has previously debuted. But if that is the case, there is still a real question about why Microsoft opted to use GPT-4 rather than a smaller, but better-behaved system to power the new Bing. And frankly, there is also a real question about why OpenAI might have encouraged Microsoft to use the more powerful model if it in fact realized it had more potential to behave in ways that users might find disturbing. The Microsoft folks may have, like many A.I. researchers before them, become blinded by stellar benchmark performance that can convey bragging rights among other A.I. developers, but which are a poor proxy for what real human users want.)

What is certain is that if Microsoft doesn’t fix this soon—and if someone else, such as Google, which is hard at work trying to hone its search chatbot for imminent release, or any of the others, including startups such as Perplexity and You.com, that have debuted their own chatbots, shows that their chatbot can hold long dialogues without it turning into Damien—then Microsoft risks losing its first mover advantage in the new search wars.

Also, let’s just take a moment to appreciate the irony that it’s Microsoft, a company that once prided itself, not without reason, on being among the most responsible of the big technology companies, which has now tossed us all back to the bad old “move fast and break things” days of the early social media era—with perhaps even worse consequences. (But I guess when your CEO is obsessed with making his arch-rival “dance” it is hard for the musicians in the band to argue that maybe they shouldn’t be striking up the tune just yet.) Beyond OpenCage, Clarkesworld, and Stack Overflow, people could get hurt from incorrect advice on medicines, from abusive Sydney-like behavior that drives someone to self-harm or suicide, or from reinforcement of hateful stereotypes and tropes.

I’ve said this before in this newsletter, but I’ll say it again: Given these potential harms, now is the time for governments to step in and lay down some clear regulation about how these systems need to be built and deployed. The idea of a risk-based approach, such as that broached in the original draft of the European Union’s proposed A.I. Act, is a potential starting point. But the definitions of risk and those risk assessments should not be left entirely up to the companies themselves. There need to be clear external standards and clear accountability if those standards aren’t meant.

With that, here’s the rest of this week’s A.I. news.

Jeremy Kahn




Partnership on A.I. publishes framework for ethical creation of synthetic media. The advocacy group, which counts most big American tech companies, as well as a slew of universities and non-governmental groups among its membership, released a set of best practices and a framework for companies using A.I. to create synthetic media. Transparency is at the heart of much of the framework with the document saying that those encountering synthetic media should always be aware they are not seeing a real image and that companies using synthetic media should also, through the use of digital watermarks or other technology, make it very easy to detect synthetic media. But, as always with PAI’s frameworks, they are just recommendations with no way of enforcing compliance among the group’s membership and no call for action beyond self-governance.

Snap is releasing its own chatbot powered by ChatGPT. That’s according to a story in the tech publication The Verge. The “My AI” bot will be available to users of Snap’s subscription Snapchat Plus service for $3.99 a month. “The big idea is that in addition to talking to our friends and family every day, we’re going to talk to A.I. every day,” Snap CEO Evan Spiegel told the publication. “And this is something we’re well positioned to do as a messaging service.” Snap says it has trained the version of ChatGPT that powers “My AI” to adhere to Snap’s trust and safety guidelines and has also tried to make it harder for students to use the chatbot to cheat at school.

The International Baccalaureate allows students to use ChatGPT to craft essays. The degree program, which is used by many private international high schools, will allow students to use the OpenAI-developed chatbot to write essays so long as the students don’t attempt to pass the work off as their own, Matt Glanville, head of assessment principles and practice at the IB, told the Times of London. It said that over the long run, however, the program would reduce its reliance on take-home essays and reports in favor of in-class assignments.

Tesla pauses rollout of Full Self-Driving to new users. The company has been forced to stop rolling out its Full Self-Driving software to new drivers while it tries to fix problems with the software that the U.S. National Highway Traffic Safety Administration said were unsafe and error-prone, tech publication The Register reported. Among the problems the faulty software could cause were causing a car to drive straight through an intersection from a turn-only lane, fail to fully stop at a stop sign, and veer into on-coming traffic.

Company behind popular Lensa app sued for violating Illinois biometric data law. Prisma Labs, the company that created the popular Lensa app, which uses open-source text-to-image generative-A.I. system Stable Diffusion to create digital avatars from people’s selfies, faces a federal class action lawsuit filed in California that alleges it violates Illinois’s strict biometric data protection law by collecting and storing users’ facial geometry without consent, Bloomberg reported. Prisma Labs did not immediately respond to requests for comment on the lawsuit and its allegations.

Legal tech startup powered by Anthropic’s A.I. lands funding from prominent European founders. The company, called Robin AI, announced a $10.5 million Series A round led by Taavet Hinrikus, a co-founder of financial technology company Wise and an early engineer at Skype, and Ian Hogarth, who cofounded concert discovery site Songkick, according to a story in the European technology publication Sifted. Robin has created software, based on Anthropic’s large language models, that can draft and edit legal contracts. Anthropic was created by a team that broke away from OpenAI in 2021 and is competing with OpenAI in the creation of large “foundation” models and generative A.I. Robin is competing with a number of legal startups, including Harvey AI, which received $5 million in Series A funding, in part from OpenAI’s own startup fund, and CaseText, that have been using OpenAI’s A.I. to create “co-pilots” for the legal profession.


Meta unveils an open-source large language model family in challenge to OpenAI. The social media company is making several versions of a large language model it calls LLaMA available to academics, civil society, policymakers, and the public to use in research and to build free applications, it said in a blog post. The largest of the LLaMA models is 65 billion parameters, which is about a third of the size of OpenAI’s GPT-3, but Meta says that LLaMA performs as well or better than GPT-3 on many tasks. LLaMA comes at a time when there is growing concern that university researchers and government institutions will have difficulty using the largest class of “foundation models” because they are so large that only massive technology companies can afford to train and run them. The service terms for LLaMA state that the models cannot be used for commercial products.


Elon Musk and Tesla face a fresh lawsuit alleging his self-driving tech is a fraud—by Christiaan Hetzner

Amazon driver breaks down the A.I. system watching workers for safety violations like drinking coffee while driving and counting the times they buckle their seatbelt—by Orianna Rosa Royle

A.I. firms are trying to replace voice actors, and they’re getting help from voice actors to do it—by Steve Mollman


Sam Altman has thoughts about AGI—and people have thoughts about Sam’s thoughts. Altman, OpenAI’s cofounder and CEO, wrote a blog post four days ago in which he tried to outline OpenAI’s approach to artificial general intelligence, the über-powerful form of A.I. that OpenAI was founded to create. Altman’s blog generated a lot of attention, some of it laudatory, much of it critical. (Altman’s blog may in fact be one of the things that prompted Elon Musk to tweet that he’s been experiencing a lot of angst about AGI.) Emily Bender, the University of Washington computational linguist who has been on a mission to pierce much of the hype around today’s A.I., particularly large language models, has a scathing critique of Altman’s post. Bender’s take has received a lot of attention and is worth reading, even if you don’t agree with all of her criticism. I happen to agree with a lot of what Bender says about Altman’s rhetorical sleight-of-hand in positioning today’s LLM-based models, including ChatGPT, as being on the path to AGI. But I think there is a key paragraph buried deep in Altman’s blog that has not received as much attention as it should have. It is where Altman says the following:

We think it’s important that efforts like ours submit to independent audits before releasing new systems; we will talk about this in more detail later this year. At some point, it may be important to get independent review before starting to train future systems, and for the most advanced efforts to agree to limit the rate of growth of compute used for creating new models. We think public standards about when an AGI effort should stop a training run, decide a model is safe to release, or pull a model from production use are important. Finally, we think it’s important that major world governments have insight about training runs above a certain scale.” (Bolding mine.)

This should be much bigger news. In essence, OpenAI is beginning to tip-toe into the idea of some kind of governmental entity, perhaps even an international body, licensing the training of models above a certain size. (The line about advanced efforts “agreeing to limit the rate of growth” sounds like industry-driven self-regulation, which I doubt will work. But an international body could potentially enforce such a mechanism.) There might even be a prohibition or a temporary moratorium on the development of certain kinds of models beyond a certain size. And because these ultra-massive models require huge amounts of data center infrastructure, it might actually be possible for governments to enforce these prohibitions, much as bodies like the International Atomic Energy Agency monitors and inspects nuclear facilities around the world. These large data centers are not so easy to hide. Software might exist in the ether—but hardware is a real physical thing.

This is an idea that even the critics of large language models might be able to get behind—not because they are worried about AGI, but because they think that LLMs are hugely wasteful pieces of technology that amplify existing societal biases and historical prejudices, make global inequality worse, and ruin the planet with their massive carbon footprint. If there were a national or international body regulating the training of ultra-large models, the body could potentially take action, stepping in and doing what Bender and other critics of the current wave of LLM development have long advocated—stop further development of A.I. systems based on ultra-large models.

Meanwhile, if you do worry about AGI and its potential ramifications, having a national or international body that is at least thinking about this and how to avoid a doomsday scenario is no bad thing. We have international agreements, of various kinds, regulating nuclear technology, certain advanced biological research, and the trade in certain chemicals. It is probably time to start thinking about advanced A.I. in the same way.

Qu’est-ce que LLaMA, le rival de ChatGPT poussé par Meta


Meta se lance dans la guerre de l’IA générative avec LLaMA, son modèle de langage destiné aux intelligences artificielles.

Le vendredi 24 février 2023, Meta, la maison mère de Facebook, a affirmé sa volonté de ne pas manquer la guerre de l’IA. Face à ChatGPT et Bard, la réponse du géant des réseaux sociaux s’appellera donc LLaMA, un « nouveau modèle de langage […] conçu pour aider les chercheurs dans leurs travaux », selon Mark Zuckerberg dans un message publié sur compte Facebook.

Récemment, les intelligences artificielles génératives ont montré leur aptitude pour « générer du texte, tenir des conversations, résumer des textes, et même exécuter des tâches plus complexes telles que la résolution de théorèmes mathématiques », relève le fondateur du réseau social — et Meta n’entend pas laisser le champ libre à ses rivaux dans un secteur de plus en plus stratégique.

LLaMA, pour Large Language Model Meta AI, est, comme son nom l’indique, un modèle linguistique. Il s’agit du socle permettant d’utiliser les intelligences artificielles comme ChatGPT et les autres agents conversationnels — les chatbots, en anglais. L’arrivée de Meta dans ce domaine, qui attire toutes les convoitises depuis la sortie de ChatGPT, n’est pas anodine — et LLaMA n’est pas un modèle comme les autres.

C’est quoi un modèle de langage ?

Tout d’abord, il convient de clarifier ce qu’est un modèle de langage. Il s’agit d’un modèle statistique qui permet de prédire quel mot suivra, en se fondant sur les termes déjà inscrits. C’est grâce à cela que les large language models savent que le mot « chat » sera beaucoup plus probablement suivi par l’adjectif « noir » que par le terme « paëlla ». LLaMA est donc l’équivalent de GPT-3, le modèle qui permet à ChatGPT de fonctionner, et de LaMDA, le modèle de langage développé par Google pour alimenter Bard.

LLaMA, le modèle de langage de Meta, réfléchissant au prochain terme à taper. Allégorie. // Source : Canva

Les dernières générations de LLM contiennent des milliards de paramètres, ce qui a permis d’affiner les réponses de ChatGPT et de faire en sorte que ces réponses paraissent les plus naturelles possible, comme si l’internaute échangeait avec un autre humain. Par exemple, GPT-3 fonctionne sur 175 milliards de paramètres, et le modèle de Google, le plus large à ce jour, en comprend 540 milliards.

Quelles caractéristiques pour LLaMA de Facebook ?

LLaMA a pour particularité de ne fonctionner « que » sur 65 milliards de paramètres. C’est moins que ses rivaux, mais selon Meta, ce dimensionnement moindre serait pourtant un avantage : LLaMA est disponible en plusieurs versions (65 milliards de paramètres, et d’autres sur 33 milliards, 13 milliards et 7 milliards).

« Les modèles plus petits, comme LLaMA, sont intéressants dans le monde des modèles très large, car ils demandent beaucoup moins de puissance de calcul et de ressources », note Meta. Une taille réduite démocratiserait l’accès aux modèles de langage — et d’épargner de la capacité de traitement.

Mais selon Meta, cette relative petitesse au niveau des paramètres ne l’empêche pas d’être « plus performant » que d’autres modèles plus grands. Le papier de recherche publié pour accompagner le lancement du modèle indique ainsi que « LLaMA-13B surpasse GPT-3 sur la plupart des critères, et LLaMA-65B est compétitif avec les meilleurs modèles, Chinchilla 70B et PaLM-540B » — il s’agit de modèles développés respectivement par DeepMind, l’entreprise à l’origine d’AlphaGo, et par Google.

Difficile de donner une réponse définitive sur les capacités de LLaMA aujourd’hui : il est impossible de tester le modèle. Pour l’instant, contrairement à Chat-GPT, LLaMA n’est pas accessible à tout le monde : seuls quelques chercheurs en faisant la demande peuvent avoir accès au modèle, dont les facultés resteront à mettre à l’épreuve.

Un lama, presque comme LLaMA. // Source : Canva

Meta estime qu’il s’agit d’une énorme avancée dans l’IA, car « LLaMA est conçu pour être polyvalent et utilisé dans de nombreux cas de figure contrairement à des modèles entrainés pour une tâche spécifique ». Le papier de recherche avance également que LLaMA peut fonctionner sur un ordinateur équipé d’une seule unité de traitement graphique (GPU, que l’on retrouve dans les cartes graphiques), un niveau d’accessibilité unique pour les LLM.

Enfin, « contrairement à Chinchilla, PaLM et GPT-3, nous n’avons utilisé que des données accessibles publiquement », ce qui permettrait de rendre le modèle open-source.

Meta rejoint la guerre de l’intelligence artificielle

L’arrivée de LLaMA ne va pas avoir un impact immédiat au niveau du grand public, comme cela a pu l’être pour ChatGPT. Encore très technique et uniquement réservé aux travaux de recherche, LLaMA ne devrait pas non plus être intégré à un moteur de recherche, comme cela va être le cas pour Bard. Meta, néanmoins, se positionne sur le plan médiatique, et rappelle qu’il est aussi le coup.

En effet, cettep présentation prouve que Meta ne compte pas laisser le devant de la scène à ses deux autres adversaires dans le domaine de l’IA, OpenAI (et, donc, Microsoft), et Google. En janvier, Yann Le Cun, le chef de l’intelligence artificielle de Meta, avait d’ailleurs critiqué les prouesses de ChatGPT, estimant qu’il n’y avait « rien de révolutionnaire », et laissant sous-entendre que l’entreprise allait bientôt dévoiler quelque chose de majeur.

LLaMA n’est pas le premier projet de Meta. Les deux précédents ont été assez mal accueillis, ce qui peut expliquer la relative discrétion de l’entreprise sur le sujet, et le fait que LLaMA ne soit pas accessible au public. Sortie en août 2022, la première tentative de Meta, Blenderbot, était censé être un chatbot capable d’apprendre de ses erreurs, mais a rapidement tenu des propos antisémites et complotistes.

Quelques mois plus tard, en novembre 2022, ce fut au tour de Galactica d’être rendue publique. L’IA était cette fois spécialisée dans la rédaction d’articles scientifiques et pouvait résoudre des problèmes de math, rédiger des articles Wikipédia et écrire du code. Mais au bout de 3 jours seulement, Galactica a dû être débranchée après avoir indiqué des réponses erronées et racistes aux utilisateurs, explique Next Inpact. Est-ce que LLaMA peut-être la solution qui permettra à Meta de se démarquer pour de bon dans la guerre de l’IA ? Cela reste à voir.

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