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Llama 3

By Meta

IntermediateModel4.3K learners

3 releases.

Definition

Llama 3 is Meta's open-weight large language model family, released in 2024, spanning multiple sizes and later extended into the multimodal Llama 3.2 and 3.3 releases.

Overview

Llama 3 continued Meta's strategy of releasing capable large language models with openly available weights, following Llama 2. Initial Llama 3 models (8B and 70B parameters) launched in April 2024, with a much larger 405B-parameter model following later that year, at the time one of the largest openly released dense models and competitive with leading proprietary systems on many benchmarks. Subsequent updates in the Llama 3 family — Llama 3.1, 3.2, and 3.3 — extended the context window, improved multilingual support, and introduced smaller vision-capable variants suited to on-device and edge deployment. Because weights are published for download, Llama 3 models can be self-hosted, fine-tuned, and modified, which made them a foundational base for a large ecosystem of derivative and fine-tuned models across the open-source AI community, widely distributed via platforms like Hugging Face. Llama 3 was succeeded by Llama 4, which introduced a mixture-of-experts architecture, but Llama 3 variants remain widely deployed due to their maturity, broad tooling support, and range of available sizes.

Key Features

  • Open-weight release across multiple sizes (8B up to 405B parameters)
  • Competitive with leading proprietary models on many benchmarks at release
  • Extended through 3.1, 3.2, and 3.3 updates with longer context and multilingual gains
  • Smaller vision-capable variants for on-device and edge deployment
  • Freely downloadable and fine-tunable, unlike closed proprietary models
  • Foundation for a large ecosystem of community fine-tuned derivatives

Use Cases

Self-hosted enterprise chatbots and assistants requiring data control
Fine-tuning for domain-specific applications
On-device and edge AI using smaller Llama 3 variants
Research into model behavior, safety, and interpretability
Cost-controlled, high-volume inference via self-hosting
Base model for community and commercial derivative models

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