# kennethleungty/llama-2-open-source-llm-cpu-inference

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973 stars · 207 forks · Python · MIT

## Links

- GitHub: https://github.com/kennethleungty/Llama-2-Open-Source-LLM-CPU-Inference
- Homepage: https://towardsdatascience.com/running-llama-2-on-cpu-inference-for-document-q-a-3d636037a3d8
- awesome-repositories: https://awesome-repositories.com/repository/kennethleungty-llama-2-open-source-llm-cpu-inference.md

## Topics

`c-transformers` `chatgpt` `cpu` `cpu-inference` `deep-learning` `document-qa` `faiss` `langchain` `language-models` `large-language-models` `llama` `llama-2` `llm` `machine-learning` `natural-language-processing` `nlp` `open-source-llm` `python` `sentence-transformers` `transformers`

## Description

This project provides a framework for executing large language models and performing document-based question answering entirely on local consumer hardware. By integrating a CPU-based inference engine with a local vector database, it enables users to process information without relying on cloud-based APIs or specialized graphics processing units.

The system functions as a command-line tool that manages the full lifecycle of private information processing. It transforms local text files into searchable vector embeddings, allowing the model to retrieve relevant context and ground its generated responses in specific user-provided source material. By utilizing quantized model execution, the framework reduces memory and compute requirements to facilitate operation on standard hardware.

The project encompasses a complete pipeline for document indexing, semantic retrieval, and context-aware generation. It ensures data privacy by keeping all document ingestion, embedding generation, and model inference tasks contained within the local environment.

## Tags

### Artificial Intelligence & ML

- [CPU Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-clients/on-device-inference/cpu-inference-runtimes.md) — Provides a framework for executing large language models on standard consumer hardware without external dependencies.
- [Local Model Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/local-model-execution.md) — Executes quantized language models on local hardware to generate text responses without relying on cloud services. ([source](https://github.com/kennethleungty/llama-2-open-source-llm-cpu-inference#readme))
- [Private LLM Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/private-llm-inference.md) — Runs quantized language models on standard consumer hardware to maintain control over the execution environment.
- [Quantized Models](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/llm-quantization-frameworks/quantized-models.md) — Executes quantized language models on standard CPUs to minimize memory and compute requirements.
- [Context-Aware Retrieval](https://awesome-repositories.com/f/artificial-intelligence-ml/context-aware-retrieval.md) — Injects retrieved document segments into the model input stream to ground generated responses in source material.
- [Local Embedding Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/local-embedding-generators.md) — Transforms raw text into vector representations using local models to ensure data privacy.
- [Local Inference CLI](https://awesome-repositories.com/f/artificial-intelligence-ml/local-model-execution/local-inference-cli.md) — Provides a command-line interface for orchestrating document ingestion and local model inference tasks.
- [Vector Databases](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-databases.md) — Manages the conversion of local text documents into searchable vector embeddings for efficient retrieval.
- [Vector Embeddings](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-embeddings.md) — Processes local files to create searchable vector representations for semantic search and context-aware answering. ([source](https://github.com/kennethleungty/llama-2-open-source-llm-cpu-inference#readme))
- [Vector Retrieval Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-retrieval-systems.md) — Implements a document indexing pipeline that converts local files into searchable embeddings for context-aware retrieval.

### Part of an Awesome List

- [Local Question Answerers](https://awesome-repositories.com/f/awesome-lists/ai/question-answering/local-question-answerers.md) — Enables retrieving specific information from local documents by using semantic search and local language models.
- [Document Q&A](https://awesome-repositories.com/f/awesome-lists/ai/document-q-a.md) — Provides a command-line interface for retrieving document context and generating answers using a local language model. ([source](https://github.com/kennethleungty/llama-2-open-source-llm-cpu-inference#readme))
- [Question Answering](https://awesome-repositories.com/f/awesome-lists/ai/question-answering.md) — Retrieves relevant context from indexed documents to generate natural language answers grounded in source material. ([source](https://github.com/kennethleungty/llama-2-open-source-llm-cpu-inference#readme))

### Data & Databases

- [Semantic Document Retrieval](https://awesome-repositories.com/f/data-databases/database-management-systems/database-engines/vector-databases/vector-document-indexing/semantic-document-retrieval.md) — Fetches relevant document segments from an index using vector embeddings for semantic similarity.