Llama
Llama is a family of autoregressive large language models developed by Meta and released in multiple versions and sizes.
LLMs in Production
LLaMA is a family of autoregressive large language models released by Meta for research and development.
- How it is used
- Supports LLM experimentation, data-driven model development, fine-tuning, quantized serving, and production deployment.
- How deep it goes
- Descends from model variants and memory estimates into a simplified architecture and training workflow, while deferring full research replication.
- Examples
- Uses Llama 2 and Llama 3 variants, GPU sizing calculations, a SimpleLlama PyTorch model, training logs, and generated text.
- What you can do after
- Build it from scratch, train a simplified model, adapt it with LoRA, quantize it, and deploy it to a shareable production endpoint.
Natural Language Processing in Action, Second Edition
Llama 2 is a family of autoregressive large language models released by Meta for text generation and understanding.
- How it is used
- Deploys an open model for local text generation while illustrating the costs and limits of scaling language models.
- How deep it goes
- Explains model sizes, resource requirements, authentication, tokenization, generation, and streaming, but does not cover training internals.
- Examples
- Uses question-answer prompts, arithmetic reasoning, decoded token sequences, and a streaming generation loop.
- What you can do after
- Apply Llama 2 via Hugging Face Transformers to tokenize prompts, generate responses, decode output, and inspect reliability.
Read how Natural Language Processing in Action, Second Edition explains Llama →
Starting Data Analytics with Generative AI and Python
A family of large language models developed by Meta for generative-AI applications.
- How it is used
- Supports generative-AI applications through hosted API calls or local Python workflows.
- How deep it goes
- Explains API configuration and local execution steps, but defers detailed model internals and current library requirements.
- Examples
- Demonstrates hosted client configuration and local prompt invocation with a quantized GGUF model.
- What you can do after
- Build it from scratch by configuring a hosted Llama endpoint or invoking a downloaded model locally with Python.
Read how Starting Data Analytics with Generative AI and Python explains Llama →
Software Testing with Generative AI
Llama-2 is an open-source family of large language models released by Meta that can be downloaded and further trained.
- How it is used
- Positions Llama as an accessible foundational model for experimentation, adaptation, and AI community development.
- How deep it goes
- Defines its openness, sizes, and access trade-offs but does not detail model architecture, training procedures, or evaluation.
- Examples
- Uses 8-billion- and 70-billion-parameter variants and the need for self-sourced datasets and infrastructure as concrete examples.
- What you can do after
- Evaluate/understand Llama’s openness, model-size options, and practical requirements for training or deployment.
Read how Software Testing with Generative AI explains Llama →
Build Applications with Local AI Models on a Mac
A family of large language models developed by Meta.
- How it is used
- Serves as a starting family for selecting a local model for general-purpose tasks, coding, reasoning, and instruction following.
- How deep it goes
- Traces major releases and strengths, but does not explain model internals or walk through a Llama-specific deployment.
- Examples
- Uses a release timeline and an Ollama workflow of listing installed models, checking current tags, and downloading a chosen model.
- What you can do after
- Apply it via Ollama by checking available tags, downloading a chosen model, and comparing its suitability for a chatbot.
Read how Build Applications with Local AI Models on a Mac explains Llama →
Related concepts
- open-weight modelkind of · An open-weight model is a machine-learning model whose trained parameter values are made available for others to download, run, or modify, subject to applicable terms.
- language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- open-source modelkind of · An open-source model is a machine-learning model whose relevant artifacts, such as code or weights, are made available under terms permitting public access and specified forms of use or modification.
- Ollamaused by · Ollama is software for downloading, running, and serving language models locally.
- weighthas part · A weight is a numerical quantity assigned to an element to represent its contribution, influence, importance, strength, or resource amount in a calculation or system.
- api calluses · An API call is a programmatic request or invocation through an application programming interface to access data or invoke an operation.
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