NVIDIA-NeMo/Automodel
🚀 Pytorch Distributed native training library for LLMs/VLMs with OOTB Hugging Face support
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
Loading star history...
Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
Ranks #577 of 3,103 repositories GStars tracks this week (top 19%).
1 release in the last 90 days, the latest 52 days ago, with 142 contributors on GitHub.
Use Cases & Benefits
- Provides a PyTorch-native distributed training library for large language and vision-language models with out-of-the-box Hugging Face integration.
- Enables scalable, flexible multi-GPU and multi-node training with minimal code changes using advanced parallelism and optimized custom kernels.
- Use for fine-tuning large language models like LLaMA or Qwen on single or multi-node GPU clusters with minimal setup.
- Use for efficient supervised and parameter-efficient fine-tuning of vision-language models such as Gemma3 or Qwen2.5-VL on image-text datasets.
- Use for research and production workflows requiring seamless scaling from small experiments to massive distributed training with PyTorch DTensor and SPMD parallelism.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
Nemo AutoModel is a Pytorch DTensor‑native SPMD open-source training library under NVIDIA NeMo Framework, designed to streamline and scale training and finetuning for LLMs and VLMs. Designed for flexibility, reproducibility, and scale, NeMo AutoModel enables both small-scale experiments and massive multi-GPU, multi-node deployments for fast experimentation in research and production environments.
⚠️ Note: NeMo AutoModel is under active development. New features, improvements, and documentation updates are released regularly. We are working toward a stable release, so expect the interface to solidify over time. Your feedback and contributions are welcome, and we encourage you to follow along as new updates roll out.
TL;DR: SPMD turns “how to parallelize” into a runtime layout choice, not a code fork.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+256 stars over 82 days across 9 snapshot days; best day +12 on Aug 14.
Forks
341+108
Open issues
488+140
Compared with peers in NLP
Fastest-growing repositories GStars tracks in this cohort for the week of Oct 18, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| ruvnet/ruflo | 79.1K | +3.3K | 75 | today |
| mudler/LocalAI | 49.9K | +345 | 75 | 1 day ago |
| camel-ai/camel | 18.0K | +0 | 75 | 49 days ago |
| kyegomez/swarms | 7.3K | +0 | 45 | 662 days ago |
| aurelio-labs/semantic-router | 4.0K | +0 | 75 | today |
| NVIDIA-NeMo/Automodel | 1.0K | +0 | 57 | 52 days ago |
| Category median | 18.0K | +0 | 75 | — |
Also by NVIDIA-NeMo
Other repositories from this maintainer that GStars tracks
- Python18.6K stars
Speech
A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
- Rust3.4K stars
Switchyard
Switchyard lets LLM applications route traffic across models and providers while preserving native OpenAI and Anthropic API compatibility - enabling flexible model selection, benchmarking, and cost/performance optimization.
- Python2.3K stars
DataDesigner
🎨 NeMo Data Designer: Generate high-quality synthetic data from scratch or from seed data.
- Python2.1K stars
RL
Scalable toolkit for efficient model reinforcement
- Python1.2K stars
Gym
Evaluate and improve models and agents using environments
Discover Repositories
Search across tracked repositories by name or description