Llama4j

Llama4j is a collection of Java components designed to enable local LLM inference on the JVM. It provides all the essential building blocks - from tokenization to tensor operations - required to implement an efficient LLM inference engine in Java without external runtime dependencies.

Overview

Llama4j implements essential components for local LLM operations in pure Java, focusing on:

• Inference Engine: Core building blocks required to implement model inference
• Tokenization: Native tokenization utilities for processing text input
• GGUF File Format: API for reading and manipulating .GGUF files

Key Features

• Pure Java Implementation: All components implemented in Java without external dependencies
• Local Execution: Run models directly on your hardware, on the JVM
• Low-Level Control: Direct access to model operations and memory management
• GGUF Format Support: Read and process GGUF files for model loading
• Custom Tokenization: Java implementation of commonly used tokenization algorithms
• Memory Efficient: Fine-grained control over model loading and memory usage

Use Cases

Llama4j is designed for:

• Building custom local LLM inference engines on the JVM
• Implementing specialized inference pipelines
• Scenarios requiring deep integration with Java systems
• Applications needing precise control over model operations
• Projects requiring offline LLM capabilities

Llama4j provides low-level building blocks for LLM operations. It's designed for developers who need to implement their own inference engine or require precise control over model operations.
If you're looking to quickly integrate LLMs into your Java application without dealing with low-level details, consider using LangChain4j instead.
LangChain4j provides a high-level API for working with various LLM providers and building AI-powered applications.
Llama4j is better suited to implement a local inference backend for LangChain4j.