rename recipes to correct HF model names

Signed-off-by: Jennifer Chen <jennifchen@nvidia.com>

Author: jenchen13

CHANGELOG.rst

@@ -131,7 +131,7 @@ Changelog
 - The PTQ example scripts ``examples/llm_ptq/hf_ptq.py``, ``examples/llm_ptq/multinode_ptq.py`` and ``examples/megatron_bridge/quantize.py`` now derive their ``--qformat`` / ``--kv_cache_qformat`` (``--quant_cfg`` / ``--kv_cache_quant`` for Megatron-Bridge) CLI vocabularies by discovering the YAML presets under ``modelopt_recipes/configs/ptq/presets/{model,kv}/`` rather than carrying hardcoded ``QUANT_CFG_CHOICES`` / ``KV_QUANT_CFG_CHOICES`` tables. The discovery helper, alias table and ready-built ``QUANT_CFG_CHOICES`` / ``KV_QUANT_CFG_CHOICES`` mappings now live in ``modelopt.recipe.presets`` and are shared by all three scripts. Presets are loaded eagerly into a plain dict at import. Adding a new preset YAML makes it available on the CLI of all three with no script change — note this means each script now accepts every preset under those directories, not just a previously curated subset. All previously-supported short names (``int8_sq``, ``nvfp4_awq``, ``fp8_pb_wo``, ``nvfp4_mse``, ``w4a8_awq``, ``nvfp4_local_hessian``, ``fp8_pc_pt``, ``int8_wo``) keep working via a small deprecation alias table; new formats should be exposed as preset YAMLs (or, longer term, as full ``--recipe`` recipes).
 - Add ``configs/ptq/presets/kv/fp8_cast.yaml`` and ``configs/ptq/presets/kv/nvfp4_cast.yaml``, promoting ``fp8_cast`` / ``nvfp4_cast`` to first-class KV presets composed from the existing ``kv_fp8_cast`` / ``kv_nvfp4_cast`` unit fragments. The previous runtime ``use_constant_amax`` post-edit in ``hf_ptq.py`` is removed; ``use_constant_amax: true`` now lives in the YAML and is therefore authoritative. **Custom (out-of-tree) recipes that target a cast KV format must set ``use_constant_amax: true`` themselves on the ``[kv]_bmm_quantizer`` config** — in-tree recipes already do via the ``kv_*_cast`` units.
 - Add FP8 KV-cache cast variants for the partial-NVFP4 and weight-only general PTQ recipes: ``general/ptq/nvfp4_mlp_only-kv_fp8_cast``, ``general/ptq/nvfp4_experts_only-kv_fp8_cast``, ``general/ptq/nvfp4_omlp_only-kv_fp8_cast``, and ``general/ptq/nvfp4_weight_only-kv_fp8_cast``. These compose the same model-quant configs as their ``-kv_fp8`` siblings with the ``kv_fp8_cast`` unit (constant-amax FP8 KV cache, no KV calibration forward pass).
-- Add Nemotron-3-Super-120B-A12B PTQ recipes ``modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Super-120B-A12B/ptq/nvfp4-mse.yaml`` (MSE-mixed) and ``nvfp4-max-calib.yaml`` (max-calib mixed): NVFP4 W4A4 routed experts + FP8 per-tensor shared experts / Mamba in/out_proj + FP8 KV cache.
+- Add Nemotron-3-Super-120B-A12B PTQ recipes ``modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Super-120B-A12B-BF16/ptq/nvfp4-mse.yaml`` (MSE-mixed) and ``nvfp4-max-calib.yaml`` (max-calib mixed): NVFP4 W4A4 routed experts + FP8 per-tensor shared experts / Mamba in/out_proj + FP8 KV cache.
 - Group layerwise calibration options under a nested ``LayerwiseConfig`` and add two knobs: ``get_qdq_activations_from_prev_layer`` (correct GPTQ-Hessian vs max-calib activation semantics — defaults to True for GPTQ, False for max/mse/local_hessian) and ``save_every`` (gate per-window ``next_inputs.pt`` activation-cache writes). Legacy bool ``layerwise`` and flat ``layerwise_checkpoint_dir`` keys still work; the bool form emits a ``DeprecationWarning``.
 - Add two layerwise-calibration memory optimizations: ``calib_mutates_weights`` (set False for amax-only algorithms — max/mse/local_hessian — to skip the per-layer weight checkpoint blob and in-memory writeback, persisting only quantizer state), and meta-device skip-layer placeholders (already-calibrated layers emit zero-filled ``meta`` tensors instead of real-device buffers, eliminating their activation memory — models with real-device inter-layer ops on the hidden state are unsupported).
 - Add ``examples/alpamayo`` showing FP8, NVFP4, and AutoQuantize (mixed-precision) quantization of the Alpamayo (formerly Alpamayo-R1) ~10B vision-language-action model, with a joint VLM + diffusion calibration loop and both fake-quant and ``--real-quant`` packed-checkpoint export. See `examples/alpamayo/README.md <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/alpamayo>`_ for details.

…/Nemotron-3-Nano-4B/ptq/nvfp4_w4a16.yaml …tron-3-Nano-4B-BF16/ptq/nvfp4_w4a16.yamlmodelopt_recipes/huggingface/models/nvidia/Nemotron-3-Nano-4B/ptq/nvfp4_w4a16.yaml renamed to modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Nano-4B-BF16/ptq/nvfp4_w4a16.yaml modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Nano-4B/ptq/nvfp4_w4a16.yaml renamed to modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Nano-4B-BF16/ptq/nvfp4_w4a16.yaml

@@ -344,7 +344,7 @@ checkpoint's** quant config verbatim:
   `nvidia/Mistral-Medium-3.5-128B-NVFP4`: decoder MLP layers 4–86 use NVFP4
   W4A4, edge MLP layers 0–3 and 87 use FP8 W8A8, and all attention projections
   and the KV cache use FP8. It uses max calibration.
-- **`Nemotron-3-Super-120B-A12B/ptq/nvfp4-mse`** mirrors
+- **`Nemotron-3-Super-120B-A12B-BF16/ptq/nvfp4-mse`** mirrors
   `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4` exactly — a hybrid
   **Mamba-MoE** with a hand-mapped, **per-component** precision scheme:
   - MoE routed experts → NVFP4 W4A4, `group_size 16`, **static** weight scales
@@ -355,13 +355,17 @@ checkpoint's** quant config verbatim:
   `nvfp4-mse.yaml` uses MSE calibration with an FP8-scale sweep (matches the
   release); `nvfp4-max-calib.yaml` is the identical layer map under plain `max`
   calibration, kept for comparison.
-- **`Nemotron-3-Ultra-550B-A55B/ptq/nvfp4-4o6`** follows the same Super-style
+- **`Nemotron-3-Ultra-550B-A55B-BF16/ptq/nvfp4-4o6`** follows the same Super-style
   component map (routed experts NVFP4 W4A4 block-16; shared experts + Mamba
   `in/out_proj` + KV cache FP8; everything else BF16), but the routed-expert
   weights use **Four-over-Six (4/6)** NVFP4: an MSE search picks each weight's
   amax multiplier from `[1.0, 1.5]` (M=6 vs. M=4). Activations stay dynamic
   NVFP4 (not MSE-calibrated).
-- **`Nemotron-3-Nano-4B/ptq/nvfp4_w4a16`** mirrors the GGUF **Q4_K_M** bit
+- **`Nemotron-3.5-Lightning-30B-A3B-BF16/ptq/w4a16_nvfp4_4o6`** applies
+  Four-over-Six NVFP4 W4A16 to routed experts, shared experts, and the language
+  model head; Mamba `in/out_proj` weights and inputs plus the KV cache use FP8,
+  while attention remains BF16.
+- **`Nemotron-3-Nano-4B-BF16/ptq/nvfp4_w4a16`** mirrors the GGUF **Q4_K_M** bit
   allocation of the Nemotron-H hybrid, mapped onto NVFP4/FP8 **per layer**:
   Q4_K/Q5_0 linears → NVFP4 W4A4 (attention q/k/v/o kept uniform so export can
   fuse them), the Q6_K MLP `down_proj` layers → FP8 W8A8, embeddings → NVFP4

…Super-120B-A12B/ptq/nvfp4-max-calib.yaml …-120B-A12B-BF16/ptq/nvfp4-max-calib.yamlmodelopt_recipes/huggingface/models/nvidia/Nemotron-3-Super-120B-A12B/ptq/nvfp4-max-calib.yaml renamed to modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Super-120B-A12B-BF16/ptq/nvfp4-max-calib.yaml modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Super-120B-A12B/ptq/nvfp4-max-calib.yaml renamed to modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Super-120B-A12B-BF16/ptq/nvfp4-max-calib.yaml

@@ -29,7 +29,7 @@ pipeline:
       - --calib-size 32
     environment:
       - MLM_MODEL_CFG: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
-      - QUANT_CFG: huggingface/models/nvidia/Nemotron-3-Super-120B-A12B/ptq/nvfp4-mse
+      - QUANT_CFG: huggingface/models/nvidia/Nemotron-3-Super-120B-A12B-BF16/ptq/nvfp4-mse
       - HF_MODEL_CKPT: /hf-local/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
       # MMLU + Export run as separate tasks; quantize.sh does quantize only.
       - RUN_MMLU: "false"
@@ -52,7 +52,7 @@ pipeline:
     script: common/megatron_lm/export/export.sh
     environment:
       - MLM_MODEL_CFG: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
-      - QUANT_CFG: huggingface/models/nvidia/Nemotron-3-Super-120B-A12B/ptq/nvfp4-mse
+      - QUANT_CFG: huggingface/models/nvidia/Nemotron-3-Super-120B-A12B-BF16/ptq/nvfp4-mse
       - HF_MODEL_CKPT: /hf-local/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
       - TP: "1"
       - PP: "4"

…ron-3-Super-120B-A12B/ptq/nvfp4-mse.yaml …-Super-120B-A12B-BF16/ptq/nvfp4-mse.yamlmodelopt_recipes/huggingface/models/nvidia/Nemotron-3-Super-120B-A12B/ptq/nvfp4-mse.yaml renamed to modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Super-120B-A12B-BF16/ptq/nvfp4-mse.yaml modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Super-120B-A12B/ptq/nvfp4-mse.yaml renamed to modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Super-120B-A12B-BF16/ptq/nvfp4-mse.yaml

@@ -30,7 +30,7 @@ pipeline:
       - --calib-size 32
     environment:
       - MLM_MODEL_CFG: nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
-      - QUANT_CFG: huggingface/models/nvidia/Nemotron-3-Ultra-550B-A55B/ptq/nvfp4-4o6
+      - QUANT_CFG: huggingface/models/nvidia/Nemotron-3-Ultra-550B-A55B-BF16/ptq/nvfp4-4o6
       - HF_MODEL_CKPT: /hf-local/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
       # MMLU + Export run as separate tasks; quantize.sh does quantize only.
       - RUN_MMLU: "false"
@@ -53,7 +53,7 @@ pipeline:
     script: common/megatron_lm/export/export.sh
     environment:
       - MLM_MODEL_CFG: nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
-      - QUANT_CFG: huggingface/models/nvidia/Nemotron-3-Ultra-550B-A55B/ptq/nvfp4-4o6
+      - QUANT_CFG: huggingface/models/nvidia/Nemotron-3-Ultra-550B-A55B-BF16/ptq/nvfp4-4o6
       - HF_MODEL_CKPT: /hf-local/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
       - TP: "1"
       - PP: "12"

…ron-3-Ultra-550B-A55B/ptq/nvfp4-4o6.yaml …-Ultra-550B-A55B-BF16/ptq/nvfp4-4o6.yamlmodelopt_recipes/huggingface/models/nvidia/Nemotron-3-Ultra-550B-A55B/ptq/nvfp4-4o6.yaml renamed to modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Ultra-550B-A55B-BF16/ptq/nvfp4-4o6.yaml modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Ultra-550B-A55B/ptq/nvfp4-4o6.yaml renamed to modelopt_recipes/huggingface/models/nvidia/Nemotron-3-Ultra-550B-A55B-BF16/ptq/nvfp4-4o6.yaml

@@ -51,7 +51,7 @@ pipeline:
       - --export-default-te-spec
     environment:
       - MLM_MODEL_CFG: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
-      - QUANT_CFG: huggingface/models/nvidia/Nemotron-3.5-Lightning-30B-A3B/lightning_w4a16_nvfp4_4o6
+      - QUANT_CFG: huggingface/models/nvidia/Nemotron-3.5-Lightning-30B-A3B-BF16/ptq/w4a16_nvfp4_4o6
       - MLM_MODEL_CKPT: /cicd/megatron-lm-bf16/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-MCore
       - MLM_MODEL_SAVE: /cicd/megatron-lm/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-W4A16
       - HF_MODEL_CKPT: /hf-local/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16