Restructure CHANGELOG layerwise calibration entry

Show the two recipes separately: first the plain layerwise recipe for
the base feature, then the intermediate-progress-saving detail with the
GPTQ recipe that demonstrates it.

Signed-off-by: realAsma <akuriparambi@nvidia.com>

Author: realAsma

CHANGELOG.rst

@@ -15,7 +15,7 @@ Changelog
 - Enable PTQ workflow for the Step3.5-Flash MoE model with NVFP4 W4A4 + FP8 KV cache quantization. See `modelopt_recipes/models/Step3.5-Flash/nvfp4-mlp-only.yaml <https://github.com/NVIDIA/Model-Optimizer/blob/main/modelopt_recipes/models/Step3.5-Flash/nvfp4-mlp-only.yaml>`_ for more details.
 - Add support for vLLM fakequant reload using ModelOpt state for HF models. See `examples/vllm_serve/README.md <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/vllm_serve#load-qatptq-model-and-serve-in-vllm-wip>`_ for more details.
 - [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). Each decoder layer is materialized once per calibration step instead of per-batch, enabling larger batch sizes during PTQ. Includes per-layer checkpoint save/resume so calibration can survive cluster time limits. 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.
+- 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.
 
 **Backward Breaking Changes**