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Chenjie LuoandClaude Opus 4.7
ed0a4b175d
### What does this PR do?
Type of change: documentation
Refactor of the `evaluation` skill (`.claude/skills/evaluation/`) with
several substantive rule additions plus a significant compression pass.
**Skill rules added / tightened:**
- **Cross-check `recipes.vllm.ai` + HF model card before composing the
vLLM command.** Both sources matter; conflicts get surfaced to the user
instead of silently picked. WebFetch caveat triage (3 cases) for
JS-rendered variant tabs (this caveat bit the agent multiple times
during testing; the triage rules name the failure modes).
- **Single `deployment.command:` field** replaces separate
`tensor_parallel_size` / `data_parallel_size` / `extra_args` YAML
fields. NEL mounts the model at `/checkpoint`; Hydra interpolates
`${deployment.port}`.
- **vLLM defaults always included unless a recipe contradicts them**
(silence ≠ contradiction):
- `--max-num-batched-tokens 8192`
- `--enable-chunked-prefill`
- `--enable-expert-parallel` (MoE-only, detected via active-param suffix
or `num_experts`-like config field)
- `--max-num-seqs N` where `N = ceil(max_parallelism /
data_parallel_size)`, computed after Step 4 fills in `parallelism`.
- **Six-field evaluation params template:** `parallelism`,
`request_timeout`, `max_retries`, `max_new_tokens`, `temperature`,
`top_p`. No `top_k` / `presence_penalty` / `repetition_penalty` /
`min_p` at top level (task harnesses have their own defaults that
conflict). No per-task `max_new_tokens` overrides — one ceiling
everywhere.
- **`max_new_tokens` mandatory model-card lookup:** highest
card-recommended value wins; "card not yet checked + use generic
default" is explicitly forbidden (this was a real bug the user caught).
- **AA Index v2 (`recipes/tasks/aa/`)** is the default benchmark set for
quantized-checkpoint validation. "AA" / "Artificial Analysis" triggers
AA-only mode (no MMLU-Pro / AIME / LiveCodeBench unless asked).
- **MLflow auto-export on by default in shortcut path**, with
Hydra-interpolated `experiment_name` (`${USER}/${served_model_name}`),
`description` (embeds T / top_p / max_new_tokens), and `tags`
(string-coerced via single quotes for MLflow's tag type requirement).
Only `tracking_uri` needs user input.
- **Walltime capped at 4h** in generated configs (longer walltimes lower
scheduler priority → longer queue). Skill suggests three alternatives
for over-4h runs.
- **Example template** updated to the new conventions; `--max-model-len`
fallback bumped 32K → 131K to cover AA-LCR.
- **`tau2_bench_telecom` recipe:** `parallelism` left as `???` with
rate-limit guidance (canary 32–128, cap 512).
- **`model-card-research.md`:** output-length extraction is now a
mandatory, top-level checklist item with a cross-reference to the
SKILL.md rule.
- **`env.example`:** added `JUDGE_API_KEY` entry for AIME.
**Compression pass:** SKILL.md compressed ~670 → ~330 lines while
preserving every rule. Long prose collapsed into tables and bullets;
duplicated workflow checklist at the bottom removed.
### Usage
The skill is invoked when users ask to evaluate a model. The shortcut
path now produces a config like:
```yaml
deployment:
command: >-
vllm serve /checkpoint
--host 0.0.0.0
--port ${deployment.port}
--tensor-parallel-size 8
...
evaluation:
nemo_evaluator_config:
config:
params:
parallelism: ??? # Required — ask user
request_timeout: 3600
max_retries: 10
max_new_tokens: 81920 # from model card (highest)
temperature: 1.0
top_p: 0.95
export:
mlflow:
tracking_uri: ???
experiment_name: ${oc.env:USER}/${deployment.served_model_name}
description: '...'
tags:
framework: vllm
...
```
### Testing
Manually exercised the skill end-to-end on four models during refactor
(Qwen3.5-122B-A10B-FP8, Qwen3.6-35B-A3B-FP8, Kimi-K2.6, GLM-5.1-NVFP4)
to validate that the cross-check rules surface conflicts, the MLflow
defaults populate correctly, the AA suite excludes the right tasks, and
the walltime cap holds. Test configs are not included in this PR
(session artifacts at repo root, gitignored locally).
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ — the skill is
editorial/operational; no runtime API changes. Existing configs continue
to work.
- 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?: N/A — skill documentation;
manually exercised on four models.
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A — skill documentation only.
- Did you get Claude approval on this PR?: ❌ — happy to run `/claude
review` if maintainers want it.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
- Rewrote the evaluation workflow with a stricter end-to-end checklist,
workspace-reuse guidance, AA-only shortcut, mandatory validation steps,
iterative finalization loop, and a hard 04:00:00 walltime cap
- Enforced vLLM as a single deployment command and stricter
generation/config constraints, including exact top-level params and
model-card–derived max_new_tokens
- Updated env var guidance (JUDGE_API_KEY / INFERENCE_API_KEY), MLflow
export metadata, and registry/auth preflight flow
- Added several AA-task recipes, added new benchmark recipes, and
removed or consolidated legacy task docs
<!-- review_stack_entry_start -->
[](https://app.coderabbit.ai/change-stack/NVIDIA/Model-Optimizer/pull/1561?utm_source=github_walkthrough&utm_medium=github&utm_campaign=change_stack)
<!-- review_stack_entry_end -->
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Co-authored-by: Claude Opus 4.7 <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
- [2026/05/27] End-to-end Minitron workflow for Nemotron-3-Nano-30B-A3B: Pruning + two-phase distillation + FP8 quantization achieving 1.64× 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! | [LLMs] [diffusers] [VLMs] [onnx] [windows] | [docs] |
| Quantization Aware Training | Refine accuracy even further with a few training steps! | [Hugging Face] | [docs] |
| Pruning | Reduce your model size and accelerate inference by removing unnecessary weights! | [General] [Megatron-Bridge] | |
| Distillation | Reduce deployment model size by teaching small models to behave like larger models! | [Megatron-Bridge] [Megatron-LM] [Hugging Face] | [docs] |
| Speculative Decoding | Train draft modules to predict extra tokens during inference! | [Megatron] [Hugging Face] | [docs] |
| Sparsity | Efficiently compress your model by storing only its non-zero parameter values and their locations | [PyTorch] | [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 Quantization | View Support Matrix |
| Diffusers Quantization | View Support Matrix |
| VLM 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.
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
For AI-assisted development setup, see the agent tooling notes.
Top Contributors
Happy optimizing!