Welcome to the world of Jlama! This powerful inference engine empowers developers to seamlessly integrate large language models (LLMs) into their Java applications. Whether you are building chatbots or other AI-driven tools, Jlama offers a robust solution to enhance your projects. Below, we will guide you through the process of setting it up, using it, and troubleshooting common issues.

Features of Jlama

• Model Support:
  • Gemma Models
  • Llama, Llama2, Llama3 Models
  • Mistral, Mixtral Models
  • GPT-2 Models
  • BERT Models
  • BPE Tokenizers
  • WordPiece Tokenizers
• Key Implementations:
  • Paged Attention
  • Mixture of Experts
  • Tool Calling
  • Huggingface SafeTensors model and tokenizer format
  • Support for F32, F16, BF16 types
  • Support for Q8, Q4 model quantization
  • Fast GEMM operations
  • Distributed Inference

Jlama requires Java 20 or later and utilizes the new Vector API for optimal performance.

What is Jlama Used For?

Jlama allows developers to add LLM inference directly into their Java applications effortlessly. Imagine it as a powerful engine that makes your Java projects come alive with intelligent conversation and context-aware output.

Quick Start: Using Jlama as a Local Client

Installing Jlama as a local client can be achieved with a few simple commands. Here’s how:

Step 1: Install Jbang

If you don’t have Jbang installed, you can set it up with the following command:

curl -Ls https://sh.jbang.dev | bash -s - app setup

Step 2: Install Jlama CLI

Run the command below to install the Jlama CLI. You will be prompted to trust the source:

jbang app install -j 21 --name=jlama --force https://raw.githubusercontent.com/tjake/Jlama/main/jlama.java

Step 3: Run the CLI

Now that Jlama is installed, you can run it using:

jlama

It will display various commands, such as:

• download – Download a HuggingFace model
• quantize – Quantize the specified model
• chat – Interact with the specified model
• complete – Completes a prompt using the specified model
• restapi – Starts an OpenAI compatible REST API for interaction

Using Jlama in Your Java Project

Integrating Jlama into your Java project is straightforward. You can embed it easily using Langchain4j Integration, or manually add Jlama by including the following Maven dependencies:

<dependency>
    <groupId>com.github.tjake</groupId>
    <artifactId>jlama-core</artifactId>
    <version>$jlama.version</version>
</dependency>
<dependency>
    <groupId>com.github.tjake</groupId>
    <artifactId>jlama-native</artifactId>
    <classifier>$os.detected.name-$os.detected.arch</classifier>
    <version>$jlama.version</version>
</dependency>

Sample Code

Once you’ve set up the dependencies, you can use the code snippet below to interact with the model:

public void sample() throws IOException {
    String model = "tjakeTinyLlama-1.1B-Chat-v1.0-Jlama-Q4";
    String workingDirectory = ".models";
    String prompt = "What is the best season to plant avocados?";
    
    // Download the model or just return the local path if it's already downloaded
    File localModelPath = SafeTensorSupport.maybeDownloadModel(workingDirectory, model);
    
    // Load the model and use quantized memory
    AbstractModel m = ModelSupport.loadModel(localModelPath, DType.F32, DType.I8);
    
    PromptContext ctx;
    // Check if the model supports chat prompting
    if (m.promptSupport().isPresent()) {
        ctx = m.promptSupport().get().builder()
               .addSystemMessage("You are a helpful chatbot who writes short responses.")
               .addUserMessage(prompt)
               .build();
    } else {
        ctx = PromptContext.of(prompt);
    }
    
    System.out.println("Prompt: " + ctx.getPrompt() + "\n");
    
    // Generate a response to the prompt
    Generator.Response r = m.generate(UUID.randomUUID(), ctx, 0.0f, 256, (s, f) -> { });
    System.out.println(r.responseText);
}

In this code, we first download the model if it is not available locally, then utilize it to generate responses based on user inputs.

Troubleshooting Common Issues

If you encounter any issues during installation or usage, here are some common troubleshooting steps:

• Problem: Failed to install Jbang. Try running the command as an administrator or check your internet connection.
• Problem: Models do not download. Ensure you have the correct model name and check your HuggingFace access.
• Problem: Compilation errors in Java. Double-check that all dependencies are correctly added to your project.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Final Thoughts

The Jlama inference engine is a game-changer for Java developers looking to leverage the power of large language models. With its rich feature set and ease of integration, it opens up new possibilities for creating intelligent applications. Now, go ahead and bring your ideas to life with Jlama!