Llama

Q: How deeply does Starting Data Analytics with Generative AI and Python cover Llama?

Explains API configuration and local execution steps, but defers detailed model internals and current library requirements.

As explained in Starting Data Analytics with Generative AI and Python

A family of large language models developed by Meta for generative-AI applications.

Llama is a family of generative language models from Meta. It can serve business prompts through an OpenAI-compatible API or run locally when privacy, control, or cost matters. section 1.3.3 section 1.3.5

Choose an access pattern according to the practical constraint:

Use an API-compatible provider by changing the client’s base URL, authentication token, and model name; much of the surrounding Python code can remain unchanged. section 1.3.3
Run Llama 2 locally with a downloaded GGUF model, LangChain, and llama-cpp-python. section 1.3.5
Use quantization to reduce RAM and computation, accepting some loss of accuracy for easier execution. section 1.3.5

Directing an API client to Llama

A Python client can point at a Llama-compatible service by supplying its provider token and base URL, then selecting a valid variant such as llama-13b-chat. A local workflow instead installs LangChain and llama-cpp-python, loads a GGUF file, sends a prompt, and prints the response.

How Starting Data Analytics with Generative AI and Python covers it

Cover of Starting Data Analytics with Generative AI and Python

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Also explained in

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LLMs in ProductionLLaMA is a family of autoregressive large language models released by Meta for research and development.
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Natural Language Processing in Action, Second EditionLlama 2 is a family of autoregressive large language models released by Meta for text generation and understanding.
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Software Testing with Generative AILlama-2 is an open-source family of large language models released by Meta that can be downloaded and further trained.
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Build Applications with Local AI Models on a MacA family of large language models developed by Meta.

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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.

This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.