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Chenjie Luo
19ce447d62
### What does this PR do? Type of change: documentation SciCode's repeat count was pinned to `num_repeats: 1` in #1945, which dropped its effective sample count from the original `8` (set when the recipe was written in #1561) to `1`. #2254 later documented that a single run cannot gate on — scored single-shot at `temperature 1.0`, a paired comparison moved **3.92 pp and changed sign** once repeated, and the day-0 skill's own table records a DeepSeek-V4-Pro drop reading 2.96 pp (`REGRESSION`) at 1 run versus **-0.96 pp** (`PASS`) at 8. But #2254 left the remedy as a judgment call — *"pool until the standard error is below the threshold, or report the task `INDETERMINATE`"* — so a one-run SciCode number was still reportable. This restores the original avg-of-8 statistics as a hard requirement, without reintroducing the in-run repeats that #1945 removed for a reason (repeating inside one run multiplies exposure to code-execution sandbox errors). The task keeps `num_repeats: 1` per run; the repeat budget of 8 is spent as **8 independent submissions, reported as their mean** — 8 per side for a comparison, `INDETERMINATE` below 8. Changes: - **`recipes/tasks/aa/scicode.md`** — mandatory *8 runs* section: how to submit the 8 inside a multi-task AA config, fresh-run requirement (no `run.sub` replay off a warm cache), duplicate-score check, per-run validation before averaging, and mean + `stdev/sqrt(8)` + run-count reporting. - **`compare-results` / `day0-release`** — 8 runs per side is a floor, not a variance-dependent choice. - **`references/quantization-benchmarks.md`** — the repeat-count table still listed SciCode at `num_repeats: 8`, stale since #1945. - **`evaluation/SKILL.md`** — AA rule points at the 8 submissions; the walltime section distinguishes them from the forbidden practice of splitting a heavy task across configs to dodge the 4h cap. - **`evaluation/tests/evals.json`** — behavioral eval case for the rule. ### Usage ```bash # Full AA suite (= SciCode run 1), then SciCode alone for runs 2-8. nel run --config <cfg>.yaml for _ in $(seq 7); do nel run --config <cfg>.yaml -t ns_scicode; done # Report the mean of scicode_pass_at_1_avg-of-1_subtask_accuracy over the 8 runs, # plus stdev/sqrt(8) and the run count. ``` ### Testing - `pre-commit run --files <changed files>` — all hooks pass (`markdownlint-cli2`, `check json`, symlink sync), no hook-applied modifications. - `json.load` on `evaluation/tests/evals.json` parses; 4 cases, existing three unchanged (append-only diff, original formatting preserved). - Cross-checked every remaining SciCode reference in the plugin (`grep -rn -i scicode plugins/modelopt/`) so no doc still claims `num_repeats: 8` or a variance-dependent pool size. **Ran the protocol end-to-end** on Qwen3.8-27B-FP8 (gcp-nrt, 8xB200, `temperature 1.0`), 8 independent `nel run` submissions, all `COMPLETED`, each scoring the full 80 problems / 338 subtasks. MLflow experiment 2017: | passed/338 | 168 | 167 | 166 | 164 | 163 | 161 | 157 | 155 | |---|---|---|---|---|---|---|---|---| | score | 49.70 | 49.41 | 49.11 | 48.52 | 48.22 | 47.63 | 46.45 | 45.86 | **Result 48.11, stdev 1.39, stderr 0.49.** Pooled 1301/2704 subtasks reproduces 48.1139 exactly. This is the evidence for the change: **the spread is 3.85 pp and the worst single run sits 2.26 pp from the mean**, so against a 1% gate any one of these eight, reported alone, would have been defensible and wrong. Previously this PR rested on the variance figures inherited from #2254; it now rests on a measured pool. Also validated by that campaign: an agent given only "run a full SciCode eval, follow the evaluation skill" — with no mention of the protocol — read the recipe, submitted 8 runs and reported the mean with a standard error. And the corrected score key is what made the numbers harvestable at all; the `avg-of-1` name returns nothing. Two operational findings from the run, one of which is folded into the recipe: - An MLflow export job timed out (`nel-export-ns_scicode.0`, elapsed `00:30:11` against a `00:30:00` limit) because all 8 exports hit the CPU partition at once and each reinstalls the launcher. Caused by the fan-out this PR mandates, so the recipe now warns about it and says to re-submit `export.sbatch` rather than treat the run as lost. - Out of scope, filed for separate fix: `.claude/agents` is a 0-byte read-only placeholder, so the `monitor` skill's instruction to create a session registry under it fails with `Not a directory`. ### 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?: ✅ — added `scicode-eight-run-average` to `evaluation/tests/evals.json` - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A — agent-skill guidance, not a user-facing library change - Did you get Claude approval on this PR?: ❌ — not yet run ### Additional Information Restores the sampling behavior of #1561 while keeping the sandbox-load fix from #1945 and honoring the variance evidence from #2254. Note: `origin/feature/puzzletron_v2` still carries the pre-#1945 version of this file (`num_repeats: 8`, single submission) and rewrites it to source the repeat count from `examples/llm_eval/task_contracts.yaml`. If that branch lands it will need to be reconciled with this protocol. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Clarified SciCode evaluation requirements: eight independent, valid runs per comparison side with one repeat each. - Standardized score reporting using per-run metrics, mean, standard error, and run count. - Added provenance checks and replacement runs for invalid or replayed results, including timeout recovery. - Fewer than eight valid runs are reported as **INDETERMINATE**. - Updated benchmarking guidance to distinguish separate submissions from repeated runs. - **Tests** - Added coverage for eight-run averaging, validation, replacement runs, and **INDETERMINATE** outcomes. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Chenjie Luo <chenjiel@nvidia.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
- [2026/08/24] 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!