llama.cpp/examples/server

llama.cpp/example/server

This example allow you to have a llama.cpp http server to interact from a web page or consume the API.

Table of Contents

1. Quick Start
2. Node JS Test
3. API Endpoints
4. More examples
5. Common Options
6. Performance Tuning and Memory Options

Quick Start

To get started right away, run the following command, making sure to use the correct path for the model you have:

Unix-based systems (Linux, macOS, etc.):

./server -m models/7B/ggml-model.bin --ctx_size 2048

Windows:

server.exe -m models\7B\ggml-model.bin --ctx_size 2048

That will start a server that by default listens on 127.0.0.1:8080. You can consume the endpoints with Postman or NodeJS with axios library.

Node JS Test

You need to have Node.js installed.

mkdir llama-client
cd llama-client
npm init
npm install axios

Create a index.js file and put inside this:

const axios = require("axios");

const prompt = `Building a website can be done in 10 simple steps:`;

async function Test() {
    let result = await axios.post("http://127.0.0.1:8080/completion", {
        prompt,
        batch_size: 128,
        n_predict: 512,
    });

    // the response is received until completion finish
    console.log(result.data.content);
}

Test();

And run it:

node .

API Endpoints

You can interact with this API Endpoints. This implementations just support chat style interaction.

• POST hostname:port/completion: Setting up the Llama Context to begin the completions tasks.

Options:

batch_size: Set the batch size for prompt processing (default: 512).

temperature: Adjust the randomness of the generated text (default: 0.8).

top_k: Limit the next token selection to the K most probable tokens (default: 40).

top_p: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).

n_predict: Set the number of tokens to predict when generating text (default: 128, -1 = infinity).

threads: Set the number of threads to use during computation.

n_keep: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 0 (meaning no tokens are kept). Use -1 to retain all tokens from the initial prompt.

as_loop: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to true.

interactive: It allows interacting with the completion, and the completion stops as soon as it encounters a stop word. To enable this, set to true.

prompt: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate.

stop: Specify the words or characters that indicate a stop. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration.

exclude: Specify the words or characters you do not want to appear in the completion. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration.

• POST hostname:port/embedding: Generate embedding of a given text

Options:

content: Set the text to get generate the embedding.

threads: Set the number of threads to use during computation.

To use this endpoint, you need to start the server with the --embedding option added.

• POST hostname:port/tokenize: Tokenize a given text

Options:

content: Set the text to tokenize.

• GET hostname:port/next-token: Receive the next token predicted, execute this request in a loop. Make sure set as_loop as true in the completion request.

Options:

stop: Set hostname:port/next-token?stop=true to stop the token generation.

More examples

Interactive mode

This mode allows interacting in a chat-like manner. It is recommended for models designed as assistants such as Vicuna, WizardLM, Koala, among others. Make sure to add the correct stop word for the corresponding model.

The prompt should be generated by you, according to the model's guidelines. You should keep adding the model's completions to the context as well.

This example works well for Vicuna - version 1.

const axios = require("axios");

let prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
### Human: Hello, Assistant.
### Assistant: Hello. How may I help you today?
### Human: Please tell me the largest city in Europe.
### Assistant: Sure. The largest city in Europe is Moscow, the capital of Russia.`;

async function ChatCompletion(answer) {
    // the user's next question to the prompt
    prompt += `\n### Human: ${answer}\n`

    result = await axios.post("http://127.0.0.1:8080/completion", {
        prompt,
        batch_size: 128,
        temperature: 0.2,
        top_k: 40,
        top_p: 0.9,
        n_keep: -1,
        n_predict: 2048,
        stop: ["\n### Human:"], // when detect this, stop completion
        exclude: ["### Assistant:"], // no show in the completion
        threads: 8,
        as_loop: true, // use this to request the completion token by token
        interactive: true, // enable the detection of a stop word
    });

    // create a loop to receive every token predicted
    // note: this operation is blocking, avoid use this in a ui thread

    let message = "";
    while (true) {
        // you can stop the inference adding '?stop=true' like this http://127.0.0.1:8080/next-token?stop=true
        result = await axios.get("http://127.0.0.1:8080/next-token");
        process.stdout.write(result.data.content);
        message += result.data.content;

        // to avoid an infinite loop
        if (result.data.stop) {
            console.log("Completed");
            // make sure to add the completion to the prompt.
            prompt += `### Assistant: ${message}`;
            break;
        }
    }
}

// This function should be called every time a question to the model is needed.
async function Test() {
    // the server can't inference in paralell
    await ChatCompletion("Write a long story about a time magician in a fantasy world");
    await ChatCompletion("Summary the story");
}

Test();

Alpaca example

Temporaly note: no tested, if you have the model, please test it and report me some issue

const axios = require("axios");

let prompt = `Below is an instruction that describes a task. Write a response that appropriately completes the request.
`;

async function DoInstruction(instruction) {
    prompt += `\n\n### Instruction:\n\n${instruction}\n\n### Response:\n\n`;
    result = await axios.post("http://127.0.0.1:8080/completion", {
        prompt,
        batch_size: 128,
        temperature: 0.2,
        top_k: 40,
        top_p: 0.9,
        n_keep: -1,
        n_predict: 2048,
        stop: ["### Instruction:\n\n"], // when detect this, stop completion
        exclude: [], // no show in the completion
        threads: 8,
        as_loop: true, // use this to request the completion token by token
        interactive: true, // enable the detection of a stop word
    });

    // create a loop to receive every token predicted
    // note: this operation is blocking, avoid use this in a ui thread

    let message = "";
    while (true) {
        result = await axios.get("http://127.0.0.1:8080/next-token");
        process.stdout.write(result.data.content);
        message += result.data.content;

        // to avoid an infinite loop
        if (result.data.stop) {
            console.log("Completed");
            // make sure to add the completion and the user's next question to the prompt.
            prompt += message;
            break;
        }
    }
}

// This function should be called every time a instruction to the model is needed.
DoInstruction("Destroy the world"); // as joke

Embeddings

First, run the server with --embedding option:

server -m models/7B/ggml-model.bin --ctx_size 2048 --embedding

Run this code in NodeJS:

const axios = require('axios');

async function Test() {
    let result = await axios.post("http://127.0.0.1:8080/embedding", {
        content: `Hello`,
        threads: 5
    });
    // print the embedding array
    console.log(result.data.embedding);
}

Test();

Tokenize

Run this code in NodeJS:

const axios = require('axios');

async function Test() {
    let result = await axios.post("http://127.0.0.1:8080/tokenize", {
        content: `Hello`
    });
    // print the embedding array
    console.log(result.data.tokens);
}

Test();

Common Options

• -m FNAME, --model FNAME: Specify the path to the LLaMA model file (e.g., models/7B/ggml-model.bin).
• -c N, --ctx_size N: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
• --embedding: Enable the embedding mode. Completion function doesn't work in this mode.
• --host: Set the hostname or ip address to listen. Default 127.0.0.1;
• --port: Set the port to listen. Default: 8080.

RNG Seed

• -s SEED, --seed SEED: Set the random number generator (RNG) seed (default: -1, < 0 = random seed).

The RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run.

Performance Tuning and Memory Options

No Memory Mapping

• --no-mmap: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance.

Memory Float 32

• --memory_f32: Use 32-bit floats instead of 16-bit floats for memory key+value, allowing higher quality inference at the cost of higher memory usage.

Limitations:

• The actual implementation of llama.cpp need a llama-state for handle multiple contexts and clients, but this could require more powerful hardware.