NeMo Framework is NVIDIA's GPU accelerated, end-to-end training framework for large language models (LLMs), multi-modal models and speech models. It enables seamless scaling of training (both pretraining and post-training) workloads from single GPU to thousand-node clusters for both 🤗Hugging Face/PyTorch and Megatron models. This GitHub organization includes a suite of libraries and recipe collections to help users train models from end to end.
NeMo Framework is also a part of the NVIDIA NeMo software suite for managing the AI agent lifecycle.
- [10/6/2025]Enabling PyTorch Native Pipeline Parallelism for 🤗 Hugging Face Transformer Models
- [9/22/2025]Fine-tune Hugging Face Models Instantly with Day-0 Support with NVIDIA NeMo AutoModel
- [9/18/2025]🚀 NeMo Framework Now Supports Google Gemma 3n: Efficient Multimodal Fine-tuning Made Simple
- [10/1/2025]On-policy Distillation
- [9/27/2025]FP8 Quantization in NeMo RL
- [8/15/2025]NeMo-RL: Journey of Optimizing Weight Transfer in Large MoE Models by 10x
More to come and stay tuned!
| Installation | Checkpoint Conversion HF<>Megatron | LLM example recipes and scripts | VLM example recipes and scripts | |
|---|---|---|---|---|
| Under 1,000 GPUs | NeMo Automodel, NeMo RL | No Need | Pre-training, SFT, LoRA, DPO, GRPO | SFT, LoRA, GRPO |
| Over 1,000 GPUs | NeMo Megatron-Bridge, NeMo-RL | Conversion | Pretrain, SFT, and LoRA, DPO with megatron_cfg, GRPO with megatron_cfg | SFT, LoRA, GRPO megatron config |
Visit the individual repos to find out more 🔍, raise 🐛, contribute ✍️ and participate in discussion forums 🗣️!
| Repo | Key Functionality & Documentation Link | Training Loop | Training Backends | Infernece Backends | Model Coverage | Container |
|---|---|---|---|---|---|---|
| NeMo Megatron-Bridge | Pretraining, LoRA, SFT | PyT native loop | Megatron-core | NA | LLM & VLM | NeMo Framework Container |
| NeMo AutoModel | Pretraining, LoRA, SFT | PyT native loop | PyTorch | NA | LLM, VLM, Omni, VFM | NeMo AutoModel Container |
| Previous NeMo ->will repurpose to focus on Speech | Pretraining,SFT | PyTorch Lightning Loop | Megatron-core & PyTorch | RIVA | Speech | NA |
| NeMo RL | SFT, RL | PyT native loop | Megatron-core & PyTorch | vLLM | LLM, VLM | NeMo RL container |
| NeMo Gym | RL Environment, integrate with RL Framework | NA | NA | NA | NA | NeMo RL Container (WIP) |
| NeMo Aligner (deprecated) | SFT, RL | PyT Lightning Loop | Megatron-core | TRTLLM | LLM | NA |
| NeMo Curator | Data curation | NA | NA | NA | Agnostic | NeMo Curator Container |
| NeMo Evaluator | Model evaluation | NA | NA | Agnostic | NeMo Framework Container | |
| NeMo Export-Deploy | Export to Production | NA | NA | vLLM, TRT, TRTLLM, ONNX | Agnostic | NeMo Framework Container |
| NeMo Run | Experiment launcher | NA | NA | NA | Agnostic | NeMo Framework Container |
| NeMo Guardrails | Guardrail model response | NA | NA | NA | NA | |
| NeMo Skills | Reference pipeline for SDG & Eval | NA | NA | NA | Agnostic | NA |
| NeMo Emerging Optimizers | Collection of Optimizers | NA | Agnostic | NA | NA | NA |
| NeMo DFM (WIP) | Diffusion foundation model training | PyT native loop | Megatron-core and PyTorch | PyTorch | VFM, Diffusion | TBD |
| NeMotron | Developer asset hub for nemotron models | NA | NA | NA | Nemotron models | NA |
| NeMo Data-designer | Synthetic data generation toolkit | NA | NA | NA | NA | NA |
The NeMo GitHub Org and its repo collections are created to address the following problems
- Need for composability: The Previous NeMo is monolithic and encompasses too many things, making it hard for users to find what they need. Container size is also an issue. Breaking down the Monolithic repo into a series of functional-focused repos to facilitate code discovery.
- Need for customizability: The Previous NeMo uses PyTorch Lighting as the default trainer loop, which provides some out of the box functionality but making it hard to customize. NeMo Megatron-Bridge, NeMo AutoModel, and NeMo RL have adopted pytorch native custom loop to improve flexibility and ease of use for developers.
Apache 2.0 licensed with third-party attributions documented in each repository.
