Important
You are viewing the NeMo 2.0 documentation. This release introduces significant changes to the API and a new library, NeMo Run. We are currently porting all features from NeMo 1.0 to 2.0. For documentation on previous versions or features not yet available in 2.0, please refer to the NeMo 24.07 documentation.
Overview#
NVIDIA NeMo Framework is a scalable and cloud-native generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (e.g. Automatic Speech Recognition and Text-to-Speech). It enables users to efficiently create, customize, and deploy new generative AI models by leveraging existing code and pre-trained model checkpoints.
Setup Instructions: Install NeMo Framework
Large Language Models and Multimodal Models#
NeMo Framework provides end-to-end support for developing Large Language Models (LLMs) and Multimodal Models (MMs). It provides the flexibility to be used on-premises, in a data-center, or with your preferred cloud provider. It also supports execution on SLURM or Kubernetes enabled environments.
Data Curation#
NeMo Curator [1] is a Python library that includes a suite of modules for data-mining and synthetic data generation. They are scalable and optimized for GPUs, making them ideal for curating natural language data to train or fine-tune LLMs. With NeMo Curator, you can efficiently extract high-quality text from extensive raw web data sources.
Training and Customization#
NeMo Framework provides tools for efficient training and customization of LLMs and Multimodal models. It includes default configurations for compute cluster setup, data downloading, and model hyperparameters, which can be adjusted to train on new datasets and models. In addition to pre-training, NeMo supports both Supervised Fine-Tuning (SFT) and Parameter Efficient Fine-Tuning (PEFT) techniques like LoRA, Ptuning, and more.
Two options are available to launch training in NeMo - using the NeMo 2.0 API interface or with NeMo Run.
With NeMo Run (Recommended): NeMo Run provides an interface to streamline configuration, execution and management of experiments across various compute environments. This includes launching jobs on your workstation locally or on big clusters - both SLURM enabled or Kubernetes in a cloud environment.
Using the NeMo 2.0 API: This method works well with a simple setup involving small models, or if you are interested in writing your own custom dataloader, training loops, or change model layers. It gives you more flexibility and control over configurations, and makes it easy to extend and customize configurations programmatically.
Alignment#
NeMo-Aligner [1] is a scalable toolkit for efficient model alignment. The toolkit has support for state-of-the-art model alignment algorithms such as SteerLM, DPO, Reinforcement Learning from Human Feedback (RLHF), and much more. These algorithms enable users to align language models to be more safe, harmless, and helpful.
All the NeMo-Aligner checkpoints are cross-compatible with the NeMo ecosystem, allowing for further customization and inference deployment.
Step-by-step workflow of all three phases of RLHF on a small GPT-2B model:
In addition, we demonstrate support for various other novel alignment methods:
Check out the documentation for more information:
Multimodal Models#
NeMo Framework provides optimized software to train and deploy state-of-the-art multimodal models across several categories: Multimodal Language Models, Vision-Language Foundations, Text-to-Image models, and Beyond 2D Generation using Neural Radiance Fields (NeRF).
Each category is designed to cater to specific needs and advancements in the field, leveraging cutting-edge models to handle a wide range of data types, including text, images, and 3D models.
Note
We are migrating support for multimodal models from NeMo 1.0 to NeMo 2.0. If you want to explore this domain in the meantime, please refer to the documentation for the NeMo 24.07 (previous) release.
Deployment and Inference#
NeMo Framework provides various paths for LLM inference, catering to different deployment scenarios and performance needs.
Deploy with NVIDIA NIM#
NeMo Framework seamlessly integrates with enterprise-level model deployment tools through NVIDIA NIM. This integration is powered by NVIDIA TensorRT-LLM ensuring optimized and scalable inference.
For more information, visit the NVIDIA website.
Deploy with TensorRT-LLM or vLLM#
NeMo Framework offers scripts and APIs to export models to two inference optimized libraries, TensorRT-LLM and vLLM, and to deploy the exported model with the NVIDIA Triton Inference Server.
For scenarios requiring optimized performance, NeMo models can leverage TensorRT-LLM, a specialized library for accelerating and optimizing LLM inference on NVIDIA GPUs. This process involves converting NeMo models into a format compatible with TensorRT-LLM using the nemo.export module.
Supported Models#
Large Language Models |
Pretraining & SFT |
PEFT |
Alignment |
FP8 Training Convergence |
TRT/TRTLLM |
Convert To & From Hugging Face |
Evaluation |
|---|---|---|---|---|---|---|---|
Yes |
Yes |
x |
Yes (partially verified) |
Yes |
Both |
Yes |
|
Yes |
Yes |
x |
Yes (unverified) |
Yes |
Both |
Yes |
|
Yes |
x |
x |
Yes (unverified) |
x |
Both |
Yes |
|
Yes |
x |
x |
Yes (unverified) |
x |
Both |
Yes |
In addition to the ones listed above, support for the following models is coming soon: Baichuan, Gemma, Gemma 2, ChatGLM, Starcoder, Starcoder2, Qwen2, and T5.
Speech AI#
Developing conversational AI models is a complex process that involves defining, constructing, and training models within particular domains. This process typically requires several iterations to reach a high level of accuracy. It often involves multiple iterations to achieve high accuracy, fine-tuning on various tasks and domain-specific data, ensuring training performance, and preparing models for inference deployment.
NeMo Framework provides support for the training and customization of Speech AI models. This includes tasks like Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis. It offers a smooth transition to enterprise-level production deployment with NVIDIA Riva. To assist developers and researchers, NeMo Framework includes state-of-the-art pre-trained checkpoints, tools for reproducible speech data processing, and features for interactive exploration and analysis of speech datasets. The components of the NeMo Framework for Speech AI are as follows:
- Training and Customization
NeMo Framework contains everything needed to train and customize speech models (ASR, Speech Classification, Speaker Recognition, Speaker Diarization, and TTS) in a reproducible manner.
- SOTA Pre-trained Models
NeMo Framework provides state-of-the-art recipes and pre-trained checkpoints of several ASR and TTS models, as well as instructions on how to load them.
- Speech Tools
NeMo Framework provides a set of tools useful for developing ASR and TTS models, including:
NeMo Forced Aligner (NFA) for generating token-, word- and segment-level timestamps of speech in audio using NeMo’s CTC-based Automatic Speech Recognition models.
Speech Data Processor (SDP), a toolkit for simplifying speech data processing. It allows you to represent data processing operations in a config file, minimizing boilerplate code, and allowing reproducibility and shareability.
Speech Data Explorer (SDE), a Dash-based web application for interactive exploration and analysis of speech datasets.
Dataset creation tool which provides functionality to align long audio files with the corresponding transcripts and split them into shorter fragments that are suitable for Automatic Speech Recognition (ASR) model training.
Comparison Tool for ASR Models to compare predictions of different ASR models at word accuracy and utterance level.
ASR Evaluator for evaluating the performance of ASR models and other features such as Voice Activity Detection.
Text Normalization Tool for converting text from the written form to the spoken form and vice versa (e.g. “31st” vs “thirty first”).
- Path to Deployment
NeMo models that have been trained or customized using the NeMo Framework can be optimized and deployed with NVIDIA Riva. Riva provides containers and Helm charts specifically designed to automate the steps for push-button deployment.
Getting Started with Speech AI
Other Resources#
GitHub Repos#
NeMo: The main repository for the NeMo Framework
NeMo-Run: A tool to configure, launch and manage your machine learning experiments.
NeMo-Aligner: Scalable toolkit for efficient model alignment
NeMo-Curator: Scalable data pre-processing and curation toolkit for LLMs
Getting Help#
Engage with the NeMo community, ask questions, get support, or report bugs.
Programming Languages and Frameworks#
Python: The main interface to use NeMo Framework
Pytorch: NeMo Framework is built on top of PyTorch
Licenses#
NeMo Github repo is licensed under the Apache 2.0 license
NeMo Framework is licensed under the NVIDIA AI PRODUCT AGREEMENT. By pulling and using the container, you accept the terms and conditions of this license.
The NeMo Framework container contains Llama materials governed by the Meta Llama3 Community License Agreement.
Footnotes