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Meta's LLaMA releases
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LLaMA's open-weight releases are a central case study in AI governance debates about whether open-sourcing powerful models democratizes AI or dangerously proliferates dual-use capabilities, making this homepage a key reference for tracking Meta's evolving release strategy.
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Summary
Meta's LLaMA (Large Language Model Meta AI) is a series of open-weight large language models released for research and commercial use. The releases represent a major shift toward open-source AI development, enabling broad access to frontier-class language models. LLaMA models have become foundational to the open-source AI ecosystem and raise significant governance and safety considerations.
Key Points
- •LLaMA models are released as open-weight, allowing researchers and developers to download, fine-tune, and deploy them with relatively few restrictions
- •The series includes multiple generations (LLaMA 1, 2, 3) with increasing capability, context length, and safety fine-tuning
- •Open release strategy contrasts with closed models from OpenAI/Anthropic, fueling debate about open vs. closed AI development safety tradeoffs
- •Wide accessibility accelerates AI capabilities proliferation, raising dual-use and misuse concerns relevant to AI safety governance
- •Meta publishes accompanying model cards and usage policies attempting to address responsible deployment and restrict harmful use cases
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI-Driven Concentration of Power | Risk | 65.0 |
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Industry Leading, Open-Source AI | Llama
Search Documentation Products API Overview API waitlist Login Llama Stack Overview Models Models families Llama 4 Llama 3 Get started Download Learn Resources Cookbook Videos AI at Meta Blog Community Built with Llama Case studies Research AI research community Network Hugging Face GitHub Safety Llama Protections Overview Llama Defenders Program Developer use guide Llama API Stay updated Download models Build on your own terms
Optimized models for easy deployment, cost efficiency, and performance that scale to billions of users. Download models Stay updated MODELS Latest Llama models
The latest models feature native multimodality, advanced reasoning, and industry-leading context windows. Model overview Llama 4
Native multimodality leveraging early fusion to pre-train unlabeled text and vision data enabling a change in intelligence from separate, frozen multimodal weights. More details Llama 4 Maverick Natively multimodal for image and text understanding.
10M-token context for long-form work
Multimodal text + image understanding
For use cases around memory, personalization, and multi-modal applications
More details Download models Llama 4 Scout Natively multimodal offering text and visual intelligence Offers single H100 GPU efficiency
10M context window
For use cases around long document analysis
More details Download models Llama 3
The open-source AI models you can fine-tune, distill and deploy anywhere. Choose from our collection of models: Llama 3.1, Llama 3.2, Llama 3.3. More details Llama 3.3 Multilingual open source large language model.
Available in 70B
Experience 405B performance and quality at a fraction of the cost
Built for text-based use cases such as synthetic data generation
More details Download models Llama 3.2 Flexible, cost-effective, and built for edge use cases. 1B & 3B are lightweight and cost-efficient allowing you to run them anywhere
11B & 90B are flexible multimodal models that can reason on high resolution images and output text
More details Download models Llama 3.1 Open-foundation model built for flexibility and control. Available in 8B, 70B, and 405B sizes
Capabilities in general knowledge, steerability, math, tool use, and multilingual translation
Text summarization, multilingual agents, and coding use cases
More details Download models
Model optimization
Documentation overview Prompt Engineering Used in natural language processing to improve the performance of LLMs. Learn more Fine-tuning Adapting pre-trained models to perform better for a specific use case. Learn more Vision capabilities Letting the model understand and reason over images and text together. Learn more Quantization Used to reduce the computational and memory requirements of models. Learn more Distillation Teaching a smaller model to match a larger model's performance. Learn more Evaluations Automated and manual tests to systematically measure model
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