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Llama ("Large Language Model Meta AI" serving as a backronym) was a family of large language models (LLMs) released by Meta AI starting in February 2023.
The analysis highlights Applications, Art and Products as prominent areas in the source structure around Llama (language model).
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around Llama (language model) shows recurring relationship patterns in the source. For example, Llama (language model) → Foundation model, GPT, Large language model Another extracted example is Llama (language model) → Meta AI. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
llama model meta models released ai use training open-source data version versions foundation used trained license release 2024 2023 parameters
TTTA extracted 32 structured relationships around Llama (language model). Examples in this analysis include Llama (language model) → Developer → Meta AI and Llama (language model) → License → Source-available (Llama 4 Community License Agreement and Llama 4 Acceptable Use Policy). The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Llama (language model) | Developer | Meta AI | 1.00 | infobox |
| Llama (language model) | License | Source-available (Llama 4 Community License Agreement and Llama 4 Acceptable Use Policy) | 1.00 | infobox |
| Llama (language model) | Release | February 24, 2023; 3 years ago (2023-02-24) | 1.00 | infobox |
| Llama (language model) | Repository | github.com/meta-llama/llama-models | 1.00 | infobox |
| Llama (language model) | Stable release | Llama 4 Maverick / April 5, 2025; 16 months ago (2025-04-05) Llama 4 Scout / April 5, 2025; 16 months ago (2025-04-05) | 1.00 | infobox |
| Llama (language model) | Type | Large language model | 1.00 | infobox |
| Llama (language model) | Type | GPT | 1.00 | infobox |
| Llama (language model) | Type | Foundation model | 1.00 | infobox |
| Llama (language model) | Website | llama.com | 1.00 | infobox |
| Llama (language model) | Written in | Python | 1.00 | infobox |
| GPT-3 | instance of | BackgroundAfter the release of large language models | 0.80 | text |
| a focus of research was up-scaling models | instance of | BackgroundAfter the release of large language models | 0.80 | text |
The concept neighborhoods around Llama (language model) bring nearby vocabulary together. In this analysis, examples include Large, Meta and Models. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Llama (language model), one of the stronger structural bridges in this analysis connects Llama (language model) with Architecture and training. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Llama (language model) to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications, Art & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Llama (language model) · EN edition · Analysis: TopicsToTalkAbout