These days, like every major technology company, Meta has its own major generic AI model, called Lama. The Lama is somewhat unique among the major models, in which it is “open”, which means developers can download and use it, although they please (with some limitations). It is contrary to models such as the cloud of anthropic, Gemini of Google, Grake of XAI, and most chat models of OpenAII, which can only be accessed through APIs.
In the interest of giving developers’ choice, however, Meta has also participated with vendors including AWS, Google Cloud and Microsoft Azure to provide Lama’s cloud-hosted versions. In addition, the company publishes devices, libraries and dishes in its Lama Cookbook to help developers properly tune, evaluate and customize the model for their domains. Like with new generations Lama 3 and Lama 4, these capabilities have been expanded to include indigenous multimodal support and comprehensive cloud rollouts.
Here you need to know about the lama of the meta, from its abilities and versions where you can use it. We will keep this post update as the meta upgrade and introduces the new Dev tools to support the use of the model.
What is Lama?
The Lama model has a family – not just one. The latest version is Lama 4; It was released in April 2025 and includes three models:
- Scout: 17 billion active parameters, 109 billion total parameters, and a reference window of 10 million tokens.
- Maverick: 17 billion active parameters, 400 billion total parameters, and a reference window of 1 million tokens.
- Ghost: Not yet released, but 288 billion active parameters and 2 trillion total parameters will be.
(In data science, tokens are sub -witted bits of raw data, such as “fans,” “toss,” and “tick” words “fantastic.”)
The reference to a model, or reference window refers to, input data (eg, text) that the model assumes before generating output (eg, additional text). Long reference models can be prevented by “forgetting” the content of recent doors and data, and from closing the subject and incorrectly exterplation. However, long -term reference windows can also “forget” some safety railings can “forget” and be more prone to produce materials to suit the conversation, which has carried forward some users. Confusion,
For reference, 10 million reference windows that promise 4 scouts are equal to the text of about 80 average novels. 1 million reference window of LLAma 4 Maverick is equal to about eight novels.
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According to Meta, all the Lama 4 models were trained in large quantities of “large amounts of unbelled text, images and video data” on 200 languages with “comprehensive visual understanding”.
The first open-weight of the Lama 4 Scouts and Mavric Meta are originally multimodal models. They are designed using a “mixture-off-access” (MOE) architecture, which reduces computational load and improves efficiency in training and estimates. For example, Scouts have 16 experts, and Mavric has 128 experts.
LLAma 4 Behemoth consists of 16 experts, and Meta is referring to it as a teacher for small models.
The Lama makes 4 Lama on 3 series, including the 3.1 and 3.2 models used widely for the direct-tuned applications and cloud-purpose.
What can Lama do?
Like other generative AI models, Lama can demonstrate a series of various supporting functions, such as answers to coding and basic mathematics questions, along with at least 12 languages (Arabic, English, German, French, Hindi, Indonesian, Italian, Portuguese, Hindi, Spanish, Taglog, Thai and Vietnam). Most text-based workload-PDFs and analyzing large files such as spreadsheets-are within its purview, and all Lama 4 models support text, image and video input.
LLAma 4 scouts have been designed for prolonged workflow and large -scale data analysis. Maveractk is a generalist model that is better in balanceing the speed of logic power and response and suitable for coding, chatbots and technical assistants. And Behemoth is designed for advanced research, model distillation and stem tasks.
Lama models, including Lama 3.1, can be configured to take advantage of third-party applications, equipment and APIs to function. They are trained to use brave search to answer questions about recent events; Volfram Alpha API for questions related to mathematics and science; And a pythan interpreter to validate the code. However, these tools require proper configurations and are not automatically able to out of the box.
Where can I use Lama?
If you only want to chat with Lama, it is strengthening the Meta AI Chatbot experience on Facebook Messenger, WhatsApp, Instagram, Okulus and MetaAI in 40 countries. Meta AI experiences in more than 200 countries and regions use the fine-tuned versions of the Lama.
LLAma 4 models are available on scouts and maverick llama.com and meta partners, including AI developer platform Hugging Face. Behemoth is still in training. Developers building with Lama can download, use or fix models in most of the most popular cloud platforms. Meta claims that it has more than 25 partners, which host Lama, including Nvidia, Databricks, Groke, Dell and Snowflake. And Meta is not a business model when selling “access” for the openly available models, the company earns some money through revenue-sharing agreements with the model host.
Some of these partners have made additional equipment and services at the top of the Lama, including devices that refer to the model ownership data and enable them to run on low delay.
Important, Lama License How developers can deploy models: App developers with more than 700 million monthly users should request a special license from Meta that the company will grant at its discretion.
In May 2025, Meta launched a new program to encourage startups to adopt its Lama model. The Lama Meta’s Lama provides access to companies’ support and potential funding for startups.
With Lama, the meta provides equipment to make models “safe”: to use:
- Lama guardA moderation framework.
- CyberciolA cyber security risk-assessment suit.
- Lama firewallA safety railing designed to enable the construction of a safe AI system.
- Code shieldWhich provides support for estimated-time filtering of unsafe code produced by LLMS.
The Lama Guard tries to detect potentially problematic materials-or by a lama model-ordered-or-ordered materials related to criminal activity, child abuse, copyright violations, hatred, self-loss and sexual abuse-or or was fed.
He said, it is clearly not a silver pill because its previous guidelines of Meta allowed chatbott to engage in erotic and romantic chats with minors, and some reports show that that shows that Sexual conversationDevelopers can do to condition Categories of blocked materials and apply blocks in all languages, supporting the Lama.
Like the Lama Guard, the Prompt guard can block the text intended for the Lama, but only the text means “attack” on the model and behave it in an undesirable way. Meta claims that the Lama Guards can clearly defend against malicious signs (ie, the gelbreak, which attempts to go around the underlying security filters of the Lama) in addition to “Injected input“Lama Firewall works to detect and prevent risks such as quick injections, unsafe codes and risky equipment interactions.
For cybercawell, this model is less one tool than a collection of benchmarks to measure safety. A Lama model can assess cybercavale risk (according to the norms of the minimum mate) and can eliminate users in areas such as developers and “automatic social engineering” and “scaling aggressive cyber operations”.
Laama boundaries
The Lama comes with some risks and boundaries, such as all generative AI models. For example, while its most recent models have multimodal characteristics, they are mainly limited to the English language for now.
Zoom out, Meta used pirated e-books and a dataset of articles to train its Lama model. A federal judge recently biased with Meta in the copyright trial brought by 13 book writers against the company, stating that the use of copyright works for training fell under “proper use”. However, if the Lama reinforces a copyright snipet and someone uses it in a product, they can potentially violate on copyright and be responsible.
Meta also trains its AI on Instagram and Facebook posts, photos and captions, and Gets difficult for users to get out,
Programming is another area where it is intelligent to walk lightly when using the Lama. This is because the lama can be – perhaps more than its generous AI counterparts – Produce buggy or unsafe codeBut LivcodbenchA benchmark that tests the AI model on competitive coding problems, the Meta’s Lama 4 Maverals model achieved a score of 40%. This is 85% for GPT-5 high of Openai and 83% for Grocke 4 of XAI.
As usual, it is best to review any AI-related code for a human expert, which before involving service or software.
Finally, with other AI models, the Lama model is still guilty of generating admirable-dhwani but false or misleading information, whether it is coding, legal guidance, or emotional interaction with AI personality.
It was originally published on September 8, 2024, and was regularly updated with new information.
