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Motley is a marketplace to help developers access open-source LLMs

Hosting

We will use the amazing FastChat python library to host LLMs.

pip install fschat

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const { Configuration, OpenAIApi } = require("openai");


/**
 * Lists all models available on the platform.
 * @async
 * @returns {Promise<Object>} A promise that resolves to an object containing a list of models.
 * @example
 * listModels().then(models => console.log(models));
 */

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/**
 * Lists all models available on the platform.
 * @async
 * @returns {Promise<Object>} A promise that resolves to an object containing a list of models.
 * @example
 * listModels().then(models => console.log(models));
 */
async function listModels() {
    const response = await fetch("https://api.airtable.com/v0/appwJMZ6IAUnKpSwV/all", {
        headers: {

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import { createClient } from 'embedbase-js'
// initialize client
const embedbase = createClient(
  'https://api.embedbase.xyz',
  process.env.EMBEDBASE_API_KEY
)

export const config = {
    runtime: "edge",
};

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public function generateText(string $input, array $options = []): string 
{
    $url = 'https://app.embedbase.xyz/api/chat';

    $data = [
        'prompt' => $input,
        'model' => $this->modelName,
        'stream' => false,
    ];

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import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForCausalLM
from models import imagebind_model
from models.imagebind_model import ModalityType
import data


class ImageBindGPTJ(nn.Module):
    def __init__(self, imagebind):

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embedbase.dataset(datasetId).batchAdd([{
  data: 'some text',
  metadata: {
    path: 'google.com'
  }
}])

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#!/bin/bash

# This is a script that run a hello world pod on a specific node of a Kuberenetes cluster
# to test if the connection to the cluster is working.

set -e

trap clean_up INT

function clean_up() {

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import os
from embedbase import get_app
from fastapi.responses import JSONResponse
from embedbase.database.memory_db import MemoryDatabase
from embedbase.embedding.openai import OpenAI


app = (
    get_app(settings)
    .use_embedder(OpenAI(os.environ["OPENAI_API_KEY"]))

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{
  "dependencies": {
    "@llama-node/llama-cpp": "^0.0.32",
    "llama-node": "^0.0.32"
  },
  "devDependencies": {
    "@types/node": "^18.16.3"
  }
}