Llama Series by Meta
The Llama Series is Meta’s family of open-source large language models designed for diverse applications such as text generation, multimodal reasoning, and enterprise-grade AI workflows. With parameter sizes ranging from lightweight models for mobile devices to massive models for enterprise-scale deployments, the series emphasizes scalability, efficiency, and versatility. The latest addition, Llama 4, offers state-of-the-art performance in instruction-following, long-context processing, and multimodal intelligence.
Current Models in the Llama Series:
Llama 4 Scout (17B active, 109B total):
Multimodal model with 16 experts, supporting a 10 million-token context window. Optimized for real-time applications and on-device deployment.Llama 4 Maverick (17B active, 400B total):
Advanced mixture-of-experts model with 128 experts and a 1 million-token context window. Designed for complex reasoning and enterprise-scale tasks.Llama 4 Behemoth (288B active, ~2T total):
Meta’s upcoming flagship model (in training) expected to power next-generation systems.Llama 3.3 (70B):
Text-only model optimized for conversational AI, content creation, and enterprise applications. Balances performance with computational efficiency.Llama 3.2 (90B):
Multimodal model supporting text and images, built for visual reasoning tasks like document understanding and image captioning.Llama 3.2 (11B):
Mid-sized model for summarization, multilingual tasks, and conversational AI.Llama 3.2 (3B) and (1B):
Lightweight models designed for edge and mobile deployments with low latency.Llama 3.1 (405B):
The largest model in the 3.x series, supporting 128k-token context windows and multilingual understanding across eight languages.
Key Attributes:
Multimodal Capabilities: Native support for text, image, and video inputs (Llama 4).
Mixture-of-Experts Architecture: Dynamic expert selection for performance and efficiency (Scout, Maverick).
Extended Context Windows: Up to 10 million tokens (Scout) and 1 million tokens (Maverick).
Multilingual Support: English, German, French, Italian, Portuguese, Spanish, Thai, Hindi.
Instruction Tuning: Fine-tuned for reasoning, summarization, code generation, and instruction-following.
Example Use Cases:
Real-time customer service chatbots using Llama 4 Scout.
Content summarization and sentiment analysis with Llama 3.2 models.
Multimodal document analysis using Llama 4 Maverick.
On-device assistants powered by Llama 3.2 (1B) or (3B).