[Cherry-pick] PRs #1352 #1351 #1330 #1354 #1355 #1360 #1342 #1324 #1340 #1368 #1373 #1359 #1361 #1325 #1369 #1370 #1371 #1375 #1386 #1353 #1356 #1390

.claude/skills/release-cherry-pick/SKILL.md

@@ -0,0 +1,89 @@
+---
+name: release-cherry-pick
+description: Cherry-pick merged PRs labeled for a release branch into that branch, then open a PR and apply the cherry-pick-done label. Use when asked to "cherry-pick PRs for release/X.Y.Z", "pick PRs to release branch", or "cherry-pick labeled PRs".
+---
+
+# Cherry-pick PRs to a Release Branch
+
+Cherry-pick all merged `main` PRs labeled `cherry-pick-<version>` (but not `cherry-pick-done`) into the corresponding `release/<version>` branch, one by one in merge order.
+
+## Step 1 — Identify the target version
+
+Ask the user for the release version (e.g. `0.44.0`) if not already provided.
+
+Set `VERSION=<version>` for use in subsequent steps.
+
+## Step 2 — Fetch pending PRs
+
+Use the GitHub search API to list PRs that have the cherry-pick label but not cherry-pick-done, sorted by merge date ascending:
+
+```bash
+gh api "search/issues?q=repo:NVIDIA/Model-Optimizer+is:pr+is:merged+base:main+label:cherry-pick-<VERSION>+-label:cherry-pick-done&sort=updated&order=asc&per_page=50" \
+  --jq '.items[] | [.number, .title, .pull_request.merged_at] | @tsv' \
+  | sort -t$'\t' -k3
+```
+
+Present the list to the user before proceeding.
+
+## Step 3 — Set up the release branch
+
+Check out `release/<VERSION>`, creating it from the remote if it doesn't exist locally:
+
+```bash
+git fetch origin release/<VERSION>
+git checkout release/<VERSION>
+```
+
+## Step 4 — Get merge commit SHAs
+
+All PRs are squash-merged, so each has a single-parent commit. Retrieve the SHA for each PR:
+
+```bash
+gh pr view <NUM> --repo NVIDIA/Model-Optimizer --json mergeCommit --jq '.mergeCommit.oid'
+```
+
+## Step 5 — Cherry-pick in merge order
+
+Cherry-pick each commit with `-s` (DCO sign-off). GPG signing is handled automatically by the repo's git config.
+
+```bash
+git cherry-pick -s <SHA>
+```
+
+**On conflict:** Tell the user which PR caused the conflict and ask them to fix it, then continue:
+
+```bash
+git cherry-pick --continue
+```
+
+## Step 6 — Create a PR to the release branch
+
+Push the cherry-picks to a new branch and open a PR targeting `release/<VERSION>`. The PR title lists every cherry-picked PR number. The body uses `## Cherry-picked PRs` as the only heading with one `- #<NUM>` bullet per PR — no titles, no links, no extra text.
+
+```bash
+git checkout -B cherry-picks/release-<VERSION>
+git push -u origin cherry-picks/release-<VERSION>
+
+gh pr create \
+  --title "[Cherry-pick] PRs #<NUM1> #<NUM2> ..." \
+  --base release/<VERSION> \
+  --head cherry-picks/release-<VERSION> \
+  --body "$(cat <<'EOF'
+## Cherry-picked PRs
+
+- #<NUM1>
+- #<NUM2>
+...
+EOF
+)"
+```
+
+## Step 7 — Apply cherry-pick-done label
+
+Add the `cherry-pick-done` label to every PR that was successfully cherry-picked:
+
+```bash
+for pr in <NUM1> <NUM2> ...; do
+  gh pr edit $pr --repo NVIDIA/Model-Optimizer --add-label "cherry-pick-done"
+done
+```

.github/workflows/gpu_tests.yml

@@ -39,7 +39,7 @@ jobs:
       matrix:
         include:
           - example: gpu
-            timeout: 60
+            timeout: 75
             container_image: pytorch:26.01-py3
             # tests/gpu/_extensions/test_onnx_extensions.py fails for newer containers until https://github.com/tbenthompson/cppimport/pull/98
           - example: gpu_megatron

.github/workflows/unit_tests.yml

@@ -99,6 +99,7 @@ jobs:
           - {nox_session: "unit-3.10(torch_211, tf_latest)", python_version: "3.10"}
           - {nox_session: "unit-3.11(torch_211, tf_latest)", python_version: "3.11"}
           - {nox_session: "unit-3.13(torch_211, tf_latest)", python_version: "3.13"}
+          - {nox_session: "unit-3.14(torch_211, tf_latest)", python_version: "3.14"}
           - {nox_session: "unit-3.12(torch_28, tf_latest)", python_version: "3.12"}
           - {nox_session: "unit-3.12(torch_29, tf_latest)", python_version: "3.12"}
           - {nox_session: "unit-3.12(torch_210, tf_latest)", python_version: "3.12"}

CHANGELOG.rst

@@ -7,6 +7,7 @@ Changelog
 **New Features**
 
 - Support full Transformer Engine spec for Minitron pruning (``mcore_minitron``). Now we no longer need to use custom ModelOpt spec. Note that this does not affect the usage of the pruning workflow but makes pruning slightly faster and may result in slightly different pruned model because of different kernel and numerics.
+- Add end-to-end tutorial for Minitron pruning + distillation + quantization + evaluation + vLLM deployment for Nemotron-Nano-9B-v2 → Pruned 7B along with data blend preparation steps (and ablation study). See `examples/pruning/minitron/README.md <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/pruning/minitron/>`_ for details.
 - Add Puzzletron - a new algorithm for heterogeneous pruning of LLM and VLM models. See `examples/puzzletron/README.md <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/puzzletron>`_ for more details.
 - Added iterator interface using CalibrationDataReader in ONNX quantization workflow.
 - Add N:M sparse softmax support to the Triton flash attention kernel (``modelopt.torch.kernels.triton_fa``). See `examples/llm_sparsity/attention_sparsity/README.md <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/llm_sparsity/attention_sparsity>`_ for usage.
@@ -17,6 +18,7 @@ Changelog
 - [Early Testing] Add Claude Code PTQ skill (``.claude/skills/ptq/``) for agent-assisted post-training quantization. The skill guides the agent through environment detection, model support checking, format selection, and execution via the launcher or manual SLURM/Docker/bare GPU paths. Includes handling for unlisted models with custom module patching. This feature is in early testing — use with caution.
 - Add performant layerwise calibration for large models that don't fit on GPU (e.g. DeepSeek-R1, Kimi-K2). See `modelopt_recipes/general/ptq/nvfp4_experts_only-fp8_kv.yaml <https://github.com/NVIDIA/Model-Optimizer/blob/main/modelopt_recipes/general/ptq/nvfp4_experts_only-fp8_kv.yaml>`_ for usage. Layerwise calibration also supports PTQ with intermediate progress saving — useful when long PTQ runs get hit with Slurm timeouts. See `modelopt_recipes/general/ptq/nvfp4_default-none_kv_gptq.yaml <https://github.com/NVIDIA/Model-Optimizer/blob/main/modelopt_recipes/general/ptq/nvfp4_default-none_kv_gptq.yaml>`_ for usage.
 - Add implicit GEMM CUDA kernel for Conv3D with fused NVFP4 fake quantization (``modelopt.torch.quantization.src.conv``). When NVFP4 quantization is applied to an ``nn.Conv3d`` layer via ModelOpt PTQ, the implicit GEMM path is used automatically instead of cuDNN. Uses BF16 WMMA tensor cores (SM80+) with FP32 accumulation and in-kernel FP4 (E2M1) activation quantization. Grouped convolution (``groups > 1``) falls back to the default cuDNN path. Inference only — training mode falls back to cuDNN with a warning.
+- Add FP8 MHA quantization support for vision transformers. Adds an attention-aware ONNX post-processing pass (scale Mul / K-transpose move before Q, Q→DQ insertion on softmax output) in :class:`FP8QuantExporter <modelopt.onnx.export.fp8_exporter.FP8QuantExporter>`, per-instance nested-attention-wrapper skipping in the HF plugin, and ``nn.LayerNorm`` registration in ``QuantModuleRegistry`` so BMM input quantizers and LayerNorm output quantizers defined in FP8_DEFAULT_CFG are honored end-to-end. See `examples/torch_onnx/torch_quant_to_onnx.py <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/torch_onnx/torch_quant_to_onnx.py>`_ for the general timm-model quantize→ONNX workflow.
 
 **Backward Breaking Changes**
 
@@ -29,13 +31,15 @@ Changelog
 - Fix Minitron pruning (``mcore_minitron``) for MoE models. Importance estimation hooks were incorrectly registered for MoE modules and NAS step was hanging before this.
 - Fix TRT support for remote autotuning in ONNX Autotune from 10.16+ to 10.15+ and fix TRT versioning check to the ``trtexec`` version instead of the TRT Python API when using ``trtexec`` backend.
 - Exclude MatMul/Gemm nodes with K or N < 16 from ONNX INT8 and FP8 quantization. Such small-dimension GEMMs cannot efficiently use INT8/FP8 Tensor Cores and the added Q/DQ layers cause perf regressions in TensorRT. Honors Gemm ``transB`` when deriving K.
+- Fix ``nvfp4_awq`` export ``AssertionError: Modules have different quantization formats`` for MoE models (e.g. Qwen3-30B-A3B) when some experts are not exercised by the calibration data. ``awq_lite`` now applies a neutral all-ones ``pre_quant_scale`` to any expert that ends up disabled (no cache-pass tokens, NaN scales, or no search-pass tokens) so its format remains ``nvfp4_awq``, consistent with the rest of the MoE block. A warning is emitted whenever this fallback fires.
 
 **Misc**
 
 - [Security] Changed the default of ``weights_only`` to ``True`` in ``torch.load`` for secure checkpoint loading. If you need to load a checkpoint that requires unpickling arbitrary objects, first register the class in ``torch.serialization.add_safe_globals([cls])`` before loading. Added :meth:`safe_save <modelopt.torch.utils.serialization.safe_save>` and :meth:`safe_load <modelopt.torch.utils.serialization.safe_load>` API to save and load checkpoints securely.
 - Bump minimum required PyTorch version to 2.8.
 - [Experimental] Add support for transformers>=5.0, including generic PTQ and unified HF checkpoint export for fused MoE expert modules (Mixtral, Qwen2-MoE, Qwen3-MoE, Qwen3.5-MoE, DeepSeek-V3, Jamba, OLMoE, etc.).
 - Improve ``megatron_preprocess_data``: add ``--reasoning_content`` support for Nemotron v3 datasets, eliminate intermediate JSONL for HuggingFace datasets, return output file prefixes from the Python API, add gzip input support (``.jsonl.gz``), add ``--strip_newlines`` flag for plain-text pretraining data, add ``--hf_streaming`` for very large datasets (only consumed rows downloaded), and auto-shuffle when ``--hf_max_samples_per_split`` is set to avoid biased sampling.
+- Add installation support for Python 3.14. Only basic unit tests are verified for now. Production usage still defaults to Python 3.12. Python 3.10 support will be dropped in the next release.
 
 0.43 (2026-04-16)
 ^^^^^^^^^^^^^^^^^

docs/source/getting_started/_installation_for_Linux.rst

@@ -12,7 +12,7 @@ Latest Model Optimizer (``nvidia-modelopt``) currently has the following system
 +-------------------------+-----------------------------+
 | Architecture            |  x86_64, aarch64 (SBSA)     |
 +-------------------------+-----------------------------+
-| Python                  |  >=3.10,<3.14               |
+| Python                  |  >=3.10,<3.15               |
 +-------------------------+-----------------------------+
 | CUDA                    |  12.x, 13.x                 |
 +-------------------------+-----------------------------+

docs/source/getting_started/windows/_installation_standalone.rst

@@ -64,6 +64,22 @@ If you need to use any other EP for calibration, you can uninstall the existing
 
 By default, ModelOpt-Windows utilizes the `cupy-cuda12x <https://cupy.dev//>`_ tool for GPU acceleration during the INT4 ONNX quantization process. This is compatible with CUDA 12.x.
 
+If you are using CUDA 13.x, update CUDA-dependent packages manually:
+
+For official ONNX Runtime guidance, see `Nightly builds for CUDA 13.x <https://onnxruntime.ai/docs/install/#nightly-for-cuda-13x>`_.
+
+1. Uninstall ``cupy-cuda12x`` and install ``cupy-cuda13x``.
+2. Uninstall ``onnxruntime-genai-cuda`` and ``onnxruntime-gpu``.
+3. Install ONNX Runtime CUDA 13 nightly and the pre-release ``onnxruntime-genai-cuda`` package.
+
+.. code-block:: bash
+
+    pip uninstall -y cupy-cuda12x onnxruntime-genai-cuda onnxruntime-gpu
+    pip install cupy-cuda13x
+    pip install coloredlogs flatbuffers numpy packaging protobuf sympy
+    pip install --pre --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-13-nightly/pypi/simple/ onnxruntime-gpu
+    pip install --pre onnxruntime-genai-cuda
+
 **6. Verify Installation**
 
 Ensure the following steps are verified: