Update README with recent pruning/nas news (#1543)

### What does this PR do?

Update README with recent pruning/nas news.



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## Summary by CodeRabbit

* **Documentation**
* Updated the main overview to include Neural Architecture Search (NAS)
in the list of model optimization techniques and reordered the technique
list for clarity.
* Added a new "Pruning & NAS News" entry (May 13, 2026) to Latest News
with links to pruning/NAS example resources and customer stories.

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---------

Signed-off-by: Daniel Korzekwa <dkorzekwa@nvidia.com>

README.md

@@ -15,7 +15,7 @@
 
 ______________________________________________________________________
 
-**NVIDIA Model Optimizer** (referred to as **Model Optimizer**, or **ModelOpt**) is a library comprising state-of-the-art model optimization [techniques](#techniques) including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models.
+**NVIDIA Model Optimizer** (referred to as **Model Optimizer**, or **ModelOpt**) is a library comprising state-of-the-art model optimization [techniques](#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](https://huggingface.co/), [PyTorch](https://github.com/pytorch/pytorch) or [ONNX](https://github.com/onnx/onnx) model.
 
@@ -26,6 +26,12 @@ Model Optimizer is also integrated with [NVIDIA Megatron-Bridge](https://github.
 
 ## Latest News
 
+- [2026/05/13] **Pruning & NAS News**
+  - [**Puzzletron**](./examples/puzzletron): A new algorithm for heterogeneous pruning & NAS of LLM and VLM models.
+  - [**End-to-end Minitron workflow**](./examples/pruning/minitron/NVIDIA-Nemotron-Nano-9B-v2): Pruning + distillation + quantization + evaluation + vLLM deployment for Nemotron-Nano-9B-v2 → pruned 7B, including data blend preparation and an ablation study.
+  - Latest customer stories on compression:
+    - [Bielik.AI showcases an open European sovereign AI model at NVIDIA GTC](https://bielik.ai/en/nvidia-gtc-bielik-minitron-premiere/)
+    - [Domyn-Large: The journey of a European sovereign AI model for regulated industries](https://www.domyn.com/blog/domyn-large-the-journey-of-a-european-sovereign-ai-model-for-regulated-industries)
 - [2026/03/11] Model Optimizer quantized Nemotron-3-Super checkpoints are available on Hugging Face for download: [FP8](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8), [NVFP4](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4). Learn more in the [Nemotron 3 Super release blog](https://blogs.nvidia.com/blog/nemotron-3-super-agentic-ai/). Check out how to quantize Nemotron 3 models for deployment acceleration [here](./examples/llm_ptq/README.md)
 - [2026/03/11] [NeMo Megatron Bridge](https://github.com/NVIDIA-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](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/super-v3/docs/models/llm/nemotron3-super.md#quantization-ptq-and-qat) for FP8/NVFP4 quantization and HF export instructions.
 - [2025/12/11] [BLOG: Top 5 AI Model Optimization Techniques for Faster, Smarter Inference](https://developer.nvidia.com/blog/top-5-ai-model-optimization-techniques-for-faster-smarter-inference/)

pyproject.toml

@@ -15,7 +15,7 @@ fallback_version = "0.0.0"
 [project]
 name = "nvidia-modelopt"
 dynamic = ["version"]
-description = "Nvidia Model Optimizer: a unified model optimization and deployment toolkit."
+description = "Nvidia Model Optimizer: A unified library of SOTA model optimization techniques like quantization, pruning, Neural Architecture Search (NAS), distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed."
 readme = { text = "Checkout https://github.com/nvidia/Model-Optimizer for more information.", content-type = "text/markdown" }
 license = "Apache-2.0"
 license-files = ["LICENSE_HEADER"]