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Model-Optimizer/docs/source/index.rst
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realAsma b9cfdce8dc docs: add Local Hessian NVFP4 weight-scale announcement blog (#2417) 
### What does this PR do?

Type of change: documentation

Adds a Local Hessian announcement blog at
`docs/source/announcements/local-hessian.rst`, covering the NVFP4
per-block
weight-scale rule that minimizes output error instead of weight error.

Contents:

- Derivation of the per-block output-error objective and its `16x16`
local
  Hessian, with numbered equations.
- Results on Qwen3.5-9B: scale-setting comparison against max, MSE, and
  Four-over-six, plus composition with GPTQ.
- Figure 1, a grouped bar chart of the Qwen3.8-27B W4A4 candidate scores
  (BF16 in gray, the two scale rules in NVIDIA greens).
- A "Using Local Hessian" section with the config example and the
  end-to-end `hf_ptq.py` command.

Two supporting changes outside the blog:

- `docs/source/_static/announcements.css`: the `shibuya` theme has no
`span.eqno` rule, so Sphinx's default `float: right` on equation numbers
cannot share a line with MathJax's full-width display block and the
number
renders *above* the equation. This anchors it to the right of the
equation
  instead, and shrinks the table-note class.
-
`docs/source/announcements/assets/qwen3-27b-w4a4-scale-rule-accuracy.png`:
  the Figure 1 asset.

### Usage

```python
import modelopt.torch.quantization as mtq

config = {
    "quant_cfg": [...],  # quantizer configuration
    "algorithm": {"method": "local_hessian", "fp8_scale_sweep": True},
}

model = mtq.quantize(model, config, forward_loop)
```

### Testing

Documentation only; no code paths change. The `.rst` parses cleanly
under
docutils. The rendered page has not been checked with a full
`sphinx-build`,
so the equation-number CSS fix and the figure placement are worth an
eyeball
on the built docs before merge.

### Before your PR is "*Ready for review*"

- Is this change backward compatible?: N/A
- 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
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A
- Did you get Claude approval on this PR?: ❌

### Additional Information

Two items to settle before this is ready to publish:

1. The `--recipe` example points at

`modelopt_recipes/models/Qwen/Qwen3.8-27B/ptq/nvfp4_local_hessian-fp8_attn-kv_fp8_cast.yaml`,
a placeholder path derived from the existing recipe naming convention.
It
   needs to match whatever lands in #2363.
2. The tables report single-run team measurements; the blog says so and
makes
   no significance claims.

🤖 Generated with [Claude Code](https://claude.com/claude-code)


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **Documentation**
- Added guidance on NVFP4 Local-Hessian weight-scale selection,
including mathematical details, accuracy comparisons, runtime
considerations, limitations, configuration examples, and reproduction
steps.
- Updated announcement labels, headings, metadata, descriptions, and
filtering text to use “Local-Hessian.”

- **Style**
- Improved announcement formatting for equation labels, display-equation
spacing, Hessian results, table headers, and explanatory notes.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: realAsma <akuriparambi@nvidia.com>
2026-09-16 21:48:11 +00:00

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Announcements
#############
Release notes, technical updates, examples, and deployment stories from the Model Optimizer team.
.. raw:: html
<section class="announcement-section" aria-label="Announcement index">
<div class="announcement-toolbar">
<label class="announcement-search-label" for="announcement-search">Search announcements</label>
<input id="announcement-search" class="announcement-search" type="search" placeholder="Search announcements" autocomplete="off" />
<div class="announcement-tags" aria-label="Announcement tags">
<button class="announcement-tag is-active" type="button" data-tag="all" aria-pressed="true">All</button>
<button class="announcement-tag" type="button" data-tag="release" aria-pressed="false">Release</button>
<button class="announcement-tag" type="button" data-tag="autoquantize" aria-pressed="false">AutoQuantize</button>
<button class="announcement-tag" type="button" data-tag="quantization" aria-pressed="false">Quantization</button>
<button class="announcement-tag" type="button" data-tag="nvfp4" aria-pressed="false">NVFP4</button>
<button class="announcement-tag" type="button" data-tag="qad" aria-pressed="false">QAD</button>
<button class="announcement-tag" type="button" data-tag="local-hessian" aria-pressed="false">Local-Hessian</button>
<button class="announcement-tag" type="button" data-tag="speculative-decoding" aria-pressed="false">Speculative decoding</button>
<button class="announcement-tag" type="button" data-tag="dflash" aria-pressed="false">DFlash</button>
<button class="announcement-tag" type="button" data-tag="dspark" aria-pressed="false">DSpark</button>
<button class="announcement-tag" type="button" data-tag="domino" aria-pressed="false">Domino</button>
<button class="announcement-tag" type="button" data-tag="architecture" aria-pressed="false">Architecture</button>
<button class="announcement-tag" type="button" data-tag="docs" aria-pressed="false">Docs</button>
<button class="announcement-tag" type="button" data-tag="github-pages" aria-pressed="false">GitHub Pages</button>
</div>
</div>
<div class="announcement-grid" id="announcement-grid">
<article class="announcement-card" data-date="2026-09-16" data-title="Recovering W4A4 NVFP4 Accuracy with Quantization-Aware Distillation" data-summary="Weight-only NVFP4 is slower than BF16 on Blackwell; W4A4 unlocks the FP4 kernels, and QAD recovers the accuracy it costs." data-tags="quantization nvfp4 w4a4 qad distillation megatron-bridge">
<div class="announcement-card-meta">September 16, 2026 &middot; Model Optimizer Team</div>
<h2><a href="announcements/qwen36-w4a4-qad.html">Recovering W4A4 NVFP4 Accuracy with Quantization-Aware Distillation</a></h2>
<p>Weight-only NVFP4 is <em>slower</em> than BF16 on Blackwell. W4A4 beats it in 9 of 12 shapes, and QAD recovers the accuracy W4A4 costs on Qwen3.6-35B-A3B.</p>
<div class="announcement-card-tags"><span>quantization</span><span>nvfp4</span><span>w4a4</span><span>qad</span><span>distillation</span><span>megatron-bridge</span></div>
</article>
<article class="announcement-card" data-date="2026-09-09" data-title="Improving NVFP4 Accuracy with Local-Hessian Weight Scales" data-summary="How Local-Hessian selects NVFP4 block scales to minimize layer output error, and how it compares with max, MSE, Four-over-six, and GPTQ." data-tags="local-hessian quantization nvfp4 calibration modelopt">
<div class="announcement-card-meta">September 9, 2026 &middot; Model Optimizer Team</div>
<h2><a href="announcements/local-hessian.html">Improving NVFP4 Accuracy with Local-Hessian Weight Scales</a></h2>
<p>How Local-Hessian selects NVFP4 block scales to minimize layer output error, and how it compares with max, MSE, Four-over-six, and GPTQ.</p>
<div class="announcement-card-tags"><span>local-hessian</span><span>quantization</span><span>nvfp4</span><span>calibration</span><span>modelopt</span></div>
</article>
<article class="announcement-card" data-date="2026-08-24" data-title="AutoQuantize: A Fast Automatic Mixed-Precision Assignment" data-summary="AutoQuantize finds low-sensitivity mixed-precision assignments with gradient-based scoring under a modeled effective-bits budget." data-tags="autoquantize quantization mixed-precision modelopt">
<div class="announcement-card-meta">August 24, 2026 &middot; Model Optimizer Team</div>
<h2><a href="announcements/autoquantize.html">AutoQuantize: A Fast Automatic Mixed-Precision Assignment</a></h2>
<p>AutoQuantize finds low-sensitivity mixed-precision assignments with gradient-based scoring under a modeled effective-bits budget.</p>
<div class="announcement-card-tags"><span>autoquantize</span><span>quantization</span><span>mixed-precision</span><span>modelopt</span></div>
</article>
<article class="announcement-card" data-date="2026-08-13" data-title="Model Optimizer announcements are moving to GitHub Pages" data-summary="The public Model Optimizer site is gaining a PR-updated announcements landing page within the existing Sphinx documentation." data-tags="release docs github-pages">
<div class="announcement-card-meta">August 13, 2026 &middot; Model Optimizer Team</div>
<h2><a href="announcements/github-pages-announcements.html">Model Optimizer announcements are moving to GitHub Pages</a></h2>
<p>The GitHub Pages site now starts with announcements while the existing API documentation remains available in the docs navigation.</p>
<div class="announcement-card-tags"><span>release</span><span>docs</span><span>github-pages</span></div>
</article>
<article class="announcement-card" data-date="2026-07-13" data-title="DSpark vs Domino: Same DFlash Backbone, Different Correction Heads" data-summary="DSpark and Domino both build on block-parallel DFlash draft generation but diverge in their token-level correction heads." data-tags="speculative-decoding dflash dspark domino architecture">
<div class="announcement-card-meta">July 13, 2026 &middot; Model Optimizer Team</div>
<h2><a href="announcements/dspark-vs-domino.html">DSpark vs Domino: Same DFlash Backbone, Different Correction Heads</a></h2>
<p>DSpark and Domino share a DFlash backbone but make different correction-head tradeoffs: ModelOpt's <code>vanilla</code> Markov head versus a GRU.</p>
<div class="announcement-card-tags"><span>speculative-decoding</span><span>dflash</span><span>dspark</span><span>domino</span><span>architecture</span></div>
</article>
</div>
<p class="announcement-empty" id="announcement-empty" hidden>No announcements match this search.</p>
<nav class="announcement-pager" id="announcement-pager" aria-label="Announcement pages" hidden>
<button class="announcement-page-button" id="announcement-prev" type="button">Previous</button>
<span class="announcement-page-status" id="announcement-page-status"></span>
<button class="announcement-page-button" id="announcement-next" type="button">Next</button>
</nav>
</section>
.. toctree::
:hidden:
:maxdepth: 1
:caption: Announcements
self
.. toctree::
:hidden:
:glob:
:maxdepth: 1
:caption: Getting Started
getting_started/[0-9]*
Quick Start: PTQ - PyTorch <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/hf_ptq>
Quick Start: PTQ - ONNX <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/onnx_ptq>
Quick Start: PTQ - PyTorch to ONNX <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/torch_onnx>
Quick Start: PTQ - Windows <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/windows>
Quick Start: QAT and QAD <guides/quantization_aware_training>
Quick Start: Pruning <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/pruning>
Quick Start: Distillation <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/llm_distill>
Quick Start: Speculative Decoding <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/speculative_decoding>
Quick Start: Sparsity <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/llm_sparsity>
.. toctree::
:hidden:
:maxdepth: 1
:caption: Guides
guides/0_support_matrix
guides/1_quantization
guides/quantization_aware_training
guides/2_save_load
guides/3_pruning
guides/4_distillation
guides/5_speculative_decoding
guides/6_sparsity
guides/7_nas
guides/8_autocast
guides/9_autotune
guides/10_recipes
guides/11_config_system
.. toctree::
:hidden:
:glob:
:maxdepth: 1
:caption: Deployment
deployment/[0-9]*
.. toctree::
:hidden:
:glob:
:maxdepth: 1
:caption: Examples
examples/[0-9]*
.. toctree::
:hidden:
:glob:
:maxdepth: 1
:caption: Reference
reference/[0-9]*
.. toctree::
:hidden:
:glob:
:maxdepth: 1
:caption: Support
support/[0-9]*
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