Meta AI
Meta AI is Meta’s AI research and product organization, developing open models, assistants, creative tools, and infrastructure for a more connected world.
Publisher details
About Meta
Meta develops AI products, research models, and developer tools through organizations including Meta Superintelligence Labs and FAIR. Its current portfolio includes the Muse family of multimodal reasoning and media-generation models, the Llama family of open-weight language models, Segment Anything 3 for image and video segmentation, and DINOv3 for computer vision.
What license does Meta release models under?
Meta does not use one license for every model. Muse Glimmer is an open-weight model released under Apache 2.0, allowing commercial use, modification, and redistribution subject to its terms and usage policy.
Llama models use version-specific custom community licenses, while models such as DINOv3 have their own commercial licenses and requirements. Hosted models such as Muse Spark are accessed through the Meta Model API and are governed by the service’s applicable terms rather than an open-weight license.
Always review the license and usage policy for the specific model and version before downloading, modifying, or deploying it.
What are Meta models used for?
Meta’s models support a broad range of AI applications. Muse Spark is designed for multimodal reasoning, coding, computer use, tool calling, and agentic workflows. Muse Glimmer brings similar agent-focused capabilities to consumer hardware, supporting long-running local agents, reliable tool use, coding, and failure recovery.
Meta also develops specialized models for image and video generation, object segmentation and tracking, visual feature extraction, multilingual communication, scientific research, and embodied agents. Llama models remain useful for general language applications such as assistants, summarization, generation, retrieval-augmented generation, and domain-specific workflows.
Can I fine tune Meta models?
Yes. Meta describes Llama 3 as a family of models that developers can fine-tune, distill, and deploy. Meta provides guidance for full-parameter and parameter-efficient fine-tuning, as well as distillation and evaluation.
Fine-tuning support varies across Meta’s broader model portfolio. Open-weight models may be adaptable with standard training frameworks, while API-only models such as Muse Spark may not expose their weights for customer-managed fine-tuning. Check the documentation and license for the specific model before beginning a fine-tuning project.
