Welcome to the world of advanced language models! Today, we will dive into the Nemotron-4-340B-Base, a powerful language model developed by NVIDIA, and explore how to effectively deploy it in your own applications.

What is Nemotron-4-340B-Base?

Nemotron-4-340B-Base is a large language model (LLM) that boasts 340 billion parameters and supports over 50 natural languages as well as 40 coding languages. It is designed to enhance synthetic data generation pipelines, which aid in constructing training datasets for refining your own models.

Model Overview

• Pre-trained on 9 trillion tokens from diverse sources.
• Utilizes a context length of up to 4,096 tokens.
• Operates under the NVIDIA Open Model License.

How to Deploy in 3 Steps

Deployment and inference with Nemotron-4-340B-Base can be accomplished in three simple steps:

1. Create a Python script to interact with the deployed model.
2. Create a Bash script to launch the inference server.
3. Execute a Slurm job to distribute model workloads across nodes.

Step 1: Creating the Python Script

Your first task is to define the Python script named call_server.py. This script will handle requests to the model and manage text generation. Think of it as the waiter in a restaurant, taking your order (prompt) and bringing back the delicious meal (generated text).

import requests
import json

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()

def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["