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Keval MorabiaandClaude Opus 5
0058a15537
### What does this PR do? Type of change: new feature **[2/2] of a split. Based on #2544 — merge that first; this PR's diff is only the Megatron-Bridge half.** #2477 added MLflow tracking to `examples/megatron_bridge/quantize.py`. It was one of five scripts in that directory that write a checkpoint; the other four recorded nothing, so the provenance chain stopped at the PTQ checkpoint and a deployed model could not be traced back to the run that produced it. All five now take the same `--mlflow` / `--mlflow_experiment` / `--mlflow_run_name` flags, and **each declares what it records as a `Tool` beside its own flags** — the shared `mlflow_utils.py` knows none of them: | Script | Records | | --- | --- | | `prune_minitron.py` | command, arguments, log, `prune_score` metric, pointer | | `quantize.py` (#2477, moved onto the shared `Tool` in #2544) | + resolved recipe, quantizer summary | | `distill.py` | + Megatron-Bridge's per-iteration metrics and resolved config | | `export_quantized_megatron_to_hf.py` | command, arguments, log, pointer | | `export_distilled_megatron_to_hf.py` | same, one pointer per exported checkpoint | Each writes `.experiment.json` into the checkpoint it produced, and each tags what it consumed, so `prune → quantize → distill → export` is walkable both from disk and by tag query on the server. **`distill.py` opens the run and Megatron-Bridge joins it.** Its `LoggerConfig` records per-iteration metrics and the full resolved config — which a wrapper around `main()` cannot see — but nothing of `distill.py`'s own arguments and no invocation. Megatron-Bridge takes `mlflow.active_run()` when one exists, applies the tags and logs into it, so `distill_run()` opens the run on the rank Megatron-Bridge looks at (the **last** one) and the two share it. Its early exit is handled explicitly: `train()` leaves through `sys.exit(0)` on `--exit_interval`, which a blanket handler would record as `FAILED`. **The library pieces that exist for that shared run land here with their first caller**, rather than in [1/2] where they would have none: `split_tracking_credentials`, so a URI handed to something which *records* it carries no credential; `log_active_run_experiment_json`, for pointing a checkpoint at a run this process did not open; and `MlflowRunLogger._reattach`, because a co-owner can end the run first — Megatron-Bridge does, as `KILLED`, when SIGTERM arrives mid-training. Two of Megatron-Bridge's defaults are deliberately not inherited: **checkpoint artifact upload stays off** unless `--mlflow_log_checkpoints` (it pushes the whole checkpoint over HTTP after every save), and **an untracked run passes no `mlflow_*` fields at all**, since they landed in Megatron-Bridge 0.6 and sending them unconditionally would break an untracked run on an older one. ### Usage ```bash # Any of the five, same flags: torchrun --nproc_per_node 8 prune_minitron.py ... --mlflow https://<server>/ torchrun --nproc_per_node 8 quantize.py ... --mlflow https://<server>/ torchrun --nproc_per_node 8 distill.py ... --mlflow https://<server>/ torchrun --nproc_per_node 8 export_quantized_megatron_to_hf.py ... --mlflow https://<server>/ # Each checkpoint names the run that wrote it: cat /output/qad/checkpoints/.experiment.json ``` Experiments default to `$USER/megatron_bridge_{prune,quantize,distill,export,distill_export}/<model basename>-<variant>`. ### Testing - Real runs on a toy Qwen3 in one MLflow experiment covering all five Megatron-Bridge scripts and `hf_ptq` — prune, quantize, QAD distillation, quantized export, BF16 distillation, distilled export, HF PTQ — each closing `FINISHED` with the invocation, its arguments as params, its log, and a matching `.experiment.json` on disk. The chain tags line up: each stage's `source_checkpoint_path` is the previous stage's `checkpoint_path`. - `tests/examples/megatron_bridge` in `nvcr.io/nvidia/nemo:26.08`, the only lane that runs it: **76 passed**. Plus the three suites from #2544: **195 pass**. - `pre-commit run --files <changed>`: all hooks pass. - Each fix from the review rounds has a test that fails with the fix reverted: the resumed run, the foreign active run, the percent-decoded credential, the credential that cannot be moved, the rank-dependent `LoggerConfig`, the exit-callback guard, and the `iter_*` join. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ - Did you get Claude approval on this PR?: several rounds; re-requested on this head. ### Additional Information Split from a single ~1150-line PR at review's request; #2544 carries the library consolidation this builds on, and this branch is based on it. Earlier review threads here show as outdated after the rebases — they are all resolved and their fixes are in this branch. One known gap, stated in the README rather than implied: `distill.py --hf_export_path` writes a second HuggingFace checkpoint from rank 0, which is not the rank that owns the run, so it carries no pointer yet. For the same reason the uploaded `logs/distill.log` holds the last rank's output — `print_rank_0` keeps the script's own lines on rank 0 — which the README now says outright; carrying rank 0's log into a run owned by another rank needs cross-rank upload and is a follow-up. Two defects found on shared-run paths during review, both verified against the installed Megatron-Bridge 0.6 rather than its docs. Megatron-Bridge ends the run it shares with `distill.py` as `KILLED` from its SIGTERM handler (`train.py:1413`) and then leaves through `sys.exit()` (`train.py:805`), i.e. before `distill_run`'s `finally` — and MLflow's fluent calls resolve their target by *opening* a run when none is active, so a preempted distillation's log and metrics went to a second, empty run and its `KILLED` status was overwritten. Separately, an unreachable server disabled our logger but `logger_kwargs` still handed Megatron-Bridge the same URI, and `state.py` calls `set_experiment` unguarded from inside the training loop — so a best-effort `$MLFLOW_TRACKING_URI` aborted the training instead of degrading to untracked. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
NVIDIA Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, pruning, Neural Architecture Search (NAS), distillation, speculative decoding and sparsity to accelerate models.
[Input] Model Optimizer currently supports inputs of a Hugging Face, PyTorch or ONNX model.
[Optimize] Model Optimizer provides Python APIs for users to easily compose the above model optimization techniques and export an optimized quantized checkpoint. Model Optimizer is also integrated with NVIDIA Megatron-Bridge, Megatron-LM and Hugging Face Accelerate for training required inference optimization techniques.
[Export for deployment] Seamlessly integrated within the NVIDIA AI software ecosystem, the quantized checkpoint generated from Model Optimizer is ready for deployment in downstream inference frameworks like SGLang, TensorRT-LLM, TensorRT, or vLLM. The unified Hugging Face export API now supports both transformers and diffusers models.
Latest News
- [2026/09/16] End-to-end W4A4 NVFP4 + QAD tutorial for Qwen3.6-35B-A3B: NVFP4 W4A4 PTQ plus quantization-aware distillation, reaching up to 1.30x vLLM throughput over BF16 and 3.1x smaller checkpoints while recovering the accuracy W4A4 costs.
- [2026/09/09] BLOG: Improving NVFP4 Accuracy with Local-Hessian Weight Scales
- [2026/08/24] BLOG: AutoQuantize: A Fast Automatic Mixed-Precision Assignment
- [2026/08/17] BLOG: Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer: Learn how quantization-aware distillation recovers accuracy from aggressive NVFP4 quantization while reducing model size and increasing throughput.
- [2026/06/26] BLOG: Creating the NVIDIA Nemotron 3 Ultra NVFP4 Checkpoint with NVIDIA Model Optimizer: How we quantized Nemotron 3 Ultra (550B) to NVFP4 with Model Optimizer — up to 5.9× higher decode-heavy inference throughput than GLM-5.1 754B FP4 while matching BF16 accuracy. NVFP4 Checkpoint on Hugging Face.
- [2026/05/27] End-to-end Optimization tutorial for Nemotron-3-Nano-30B-A3B: Pruning + two-phase distillation + FP8 quantization achieving 2.6× vLLM throughput and 2.6× memory reduction.
- [2026/05/13] Puzzletron: A new algorithm for heterogeneous pruning & NAS of LLM and VLM models.
- [2026/04/15] Customer story: Domyn compresses Colosseum-355B → 260B using ModelOpt's Minitron pruning + distillation
- [2026/03/17] Customer story: Bielik.AI builds Bielik Minitron 7B (33% smaller, 50% faster, 90% quality retained) using ModelOpt's Minitron pruning + distillation
- [2026/03/11] Model Optimizer quantized Nemotron-3-Super checkpoints are available on Hugging Face for download: FP8, NVFP4. Learn more in the Nemotron 3 Super release blog. Check out how to quantize Nemotron 3 models for deployment acceleration here
- [2026/03/11] NeMo Megatron Bridge now supports Nemotron-3-Super quantization (PTQ and QAT) and export workflows using the Model Optimizer library. See the Quantization (PTQ and QAT) guide for FP8/NVFP4 quantization and HF export instructions.
- [2025/12/11] BLOG: Top 5 AI Model Optimization Techniques for Faster, Smarter Inference
- [2025/12/08] NVIDIA TensorRT Model Optimizer is now officially rebranded as NVIDIA Model Optimizer.
- [2025/10/07] BLOG: Pruning and Distilling LLMs Using NVIDIA Model Optimizer
- [2025/09/17] BLOG: An Introduction to Speculative Decoding for Reducing Latency in AI Inference
- [2025/09/11] BLOG: How Quantization Aware Training Enables Low-Precision Accuracy Recovery
- [2025/08/29] BLOG: Fine-Tuning gpt-oss for Accuracy and Performance with Quantization Aware Training
- [2025/08/01] BLOG: Optimizing LLMs for Performance and Accuracy with Post-Training Quantization
- [2025/06/24] BLOG: Introducing NVFP4 for Efficient and Accurate Low-Precision Inference
- [2025/05/14] NVIDIA TensorRT Unlocks FP4 Image Generation for NVIDIA Blackwell GeForce RTX 50 Series GPUs
- [2025/04/21] Adobe optimized deployment using Model-Optimizer + TensorRT leading to a 60% reduction in diffusion latency, a 40% reduction in total cost of ownership
- [2025/04/05] NVIDIA Accelerates Inference on Meta Llama 4 Scout and Maverick. Check out how to quantize Llama4 for deployment acceleration here
- [2025/03/18] World's Fastest DeepSeek-R1 Inference with Blackwell FP4 & Increasing Image Generation Efficiency on Blackwell
- [2025/02/25] Model Optimizer quantized NVFP4 models available on Hugging Face for download: DeepSeek-R1-FP4, Llama-3.3-70B-Instruct-FP4, Llama-3.1-405B-Instruct-FP4
- [2025/01/28] Model Optimizer has added support for NVFP4. Check out an example of NVFP4 PTQ here.
- [2025/01/28] Model Optimizer is now open source!
Previous News
- [2024/10/23] Model Optimizer quantized FP8 Llama-3.1 Instruct models available on Hugging Face for download: 8B, 70B, 405B.
- [2024/09/10] Post-Training Quantization of LLMs with NVIDIA NeMo and Model Optimizer.
- [2024/08/28] Boosting Llama 3.1 405B Performance up to 44% with Model Optimizer on NVIDIA H200 GPUs
- [2024/08/28] Up to 1.9X Higher Llama 3.1 Performance with Medusa
- [2024/08/15] New features in recent releases: Cache Diffusion, QLoRA workflow with NVIDIA NeMo, and more. Check out our blog for details.
- [2024/06/03] Model Optimizer now has an experimental feature to deploy to vLLM as part of our effort to support popular deployment frameworks. Check out the workflow here
- [2024/05/08] Announcement: Model Optimizer Now Formally Available to Further Accelerate GenAI Inference Performance
- [2024/03/27] Model Optimizer supercharges TensorRT-LLM to set MLPerf LLM inference records
- [2024/03/18] GTC Session: Optimize Generative AI Inference with Quantization in TensorRT-LLM and TensorRT
- [2024/03/07] Model Optimizer's 8-bit Post-Training Quantization enables TensorRT to accelerate Stable Diffusion to nearly 2x faster
- [2024/02/01] Speed up inference with Model Optimizer quantization techniques in TRT-LLM
Install
To install stable release packages for Model Optimizer with pip from PyPI:
pip install -U nvidia-modelopt[all]
Model Optimizer will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.
To install from source in editable mode with all development dependencies or to use the latest features, run:
# Clone the Model Optimizer repository
git clone git@github.com:NVIDIA/Model-Optimizer.git
cd Model-Optimizer
pip install -e .[dev]
You can also directly use NVIDIA container images, which have Model Optimizer pre-installed:
nvcr.io/nvidia/pytorch:<version>-py3nvcr.io/nvidia/nemo:<version>nvcr.io/nvidia/tensorrt-llm/release:<version>
Before pulling and using the container images, please review their respective license terms. Make sure to upgrade Model Optimizer to the latest version as described above. Visit our installation guide for more fine-grained control on installed dependencies or for alternative docker images and environment variables to setup.
Techniques
| Technique | Description | Examples | Docs |
|---|---|---|---|
| Post Training Quantization | Compress model size by 2x-4x, speeding up inference while preserving model quality! | [HF LLMs / VLMs] [Megatron-Bridge LLMs / VLMs] [Diffusers] [ONNX] [Windows] | [docs] |
| Quantization Aware Training / Distillation | Refine accuracy of quantized models even further with a few training steps! | [Hugging Face] [Megatron-Bridge] | [docs] |
| Pruning | Reduce your model parameters or memory footprint and accelerate inference by removing unnecessary weights! | [General] [Megatron-Bridge] | |
| Distillation | Reduce deployment model size by teaching small models to behave like larger models! | [Hugging Face] [Megatron-Bridge] [Megatron-LM] | [docs] |
| Speculative Decoding | Train draft modules to predict extra tokens during inference! | [Hugging Face] [Megatron-LM] | [docs] |
| Sparsity | Efficiently compress your model by storing only its non-zero parameter values and their locations | [Hugging Face] | [docs] |
Pre-Quantized Checkpoints
- Ready-to-deploy checkpoints [🤗 Hugging Face - Nvidia Model Optimizer Collection]
- Deployable on TensorRT-LLM, vLLM and SGLang
- More models coming soon!
Resources
Model Support Matrix
| Model Type | Support Matrix |
|---|---|
| LLM / VLM Quantization | View Support Matrix |
| Diffusers Quantization | View Support Matrix |
| ONNX Quantization | View Support Matrix |
| Windows Quantization | View Support Matrix |
| Quantization Aware Training | View Support Matrix |
| Pruning | View Support Matrix |
| Distillation | View Support Matrix |
| Speculative Decoding | View Support Matrix |
Deprecation Policy
Model Optimizer follows a structured approach to managing deprecated features:
- Communication: Deprecation notices are documented in the Changelog. Deprecated items include source code statements indicating deprecation timing, with runtime warnings issued upon use.
- Migration Period: Since Model Optimizer is still pre-1.0, we provide a 1-release (~1-month) migration period after deprecation. During this window, deprecated features continue functioning while issuing warnings.
- Scope: The policy addresses both complete deprecations (entire APIs removed) and partial ones (specific parameters removed while methods remain).
- Removal: Following the migration period, deprecated elements are removed in alignment with semantic versioning standards, potentially including breaking changes in minor version updates while Model Optimizer remains in 0.x.
Citation
If you use NVIDIA Model Optimizer in your research, please cite it as follows:
@misc{nvidia-modelopt,
author = {{NVIDIA Corporation}},
title = {{NVIDIA Model Optimizer}},
howpublished = {\url{https://github.com/NVIDIA/Model-Optimizer}},
year = {2024--2026},
note = {GitHub repository}
}
Contributing
Model Optimizer is now open source! We welcome any feedback, feature requests and PRs. Please read our Contributing guidelines for details on how to contribute to this project.
AI Agents
ModelOpt's agent skills can be installed from this repository and used in any workspace.
Claude Code
claude plugin marketplace add https://github.com/NVIDIA/Model-Optimizer.git
claude plugin install modelopt@modelopt
Codex
codex plugin marketplace add https://github.com/NVIDIA/Model-Optimizer.git
Then open /plugins, select the modelopt marketplace, and install modelopt.
Contributors can also use the skills directly from a checkout. See the
agent tooling notes.
Top Contributors
Happy optimizing!