Address PR comments: PyPI first for transformers, fix relative path, add CWD note

Signed-off-by: Meng Xin <mxin@nvidia.com>

Author: mxinO

.claude/skills/ptq/references/slurm-setup-ptq.md

@@ -28,10 +28,16 @@ If enroot import fails (e.g., permission errors on lustre), use pyxis inline pul
 
 ### Container dependency pitfalls
 
-**New models may need newer transformers** than what's in the container. Install from source inside the job script:
+**New models may need newer transformers** than what's in the container. Upgrade inside the job script:
 
 ```bash
-pip install git+https://github.com/huggingface/transformers.git --quiet
+pip install -U transformers
+```
+
+If the model requires an unreleased fix not yet on PyPI, fall back to installing from git (pin to a tag or commit when possible):
+
+```bash
+pip install -U "git+https://github.com/huggingface/transformers.git"
 ```
 
 **Prefer `PYTHONPATH`** to use the synced ModelOpt source instead of installing inside the container — this avoids risking dependency conflicts (e.g., `pip install -U nvidia-modelopt[hf]` can upgrade PyTorch and break other packages):
@@ -40,7 +46,7 @@ pip install git+https://github.com/huggingface/transformers.git --quiet
 export PYTHONPATH=/path/to/Model-Optimizer:$PYTHONPATH
 ```
 
-If `PYTHONPATH` doesn't work due to missing compiled extensions, fall back to `pip install -e ".[hf]" --no-build-isolation`.
+If `PYTHONPATH` doesn't work due to missing compiled extensions, fall back to `pip install -e ".[hf]" --no-build-isolation` (run from the Model-Optimizer repo root).
 
 **Watch for pip dependency conflicts** — NGC containers set `PIP_CONSTRAINT` to pin versions, causing `ResolutionImpossible` errors. Unset it first so pip can resolve freely:
 

.claude/skills/ptq/references/unsupported-models.md

@@ -49,7 +49,7 @@ print(type(cfg).__name__)
      grep -r "class <ArchName>" /tmp/transformers-main/src/transformers/models/
      ```
 
-     - **Found** → install: `pip install /tmp/transformers-main`, then re-run `AutoConfig.from_pretrained()`. For container dependency issues, see `references/slurm-setup-ptq.md` (PIP_CONSTRAINT, PYTHONPATH).
+     - **Found** → install: `pip install /tmp/transformers-main`, then re-run `AutoConfig.from_pretrained()`. For container dependency issues, see `slurm-setup-ptq.md` (PIP_CONSTRAINT, PYTHONPATH).
      - **Not found** → ask the user: *"The checkpoint uses `<ArchName>` which isn't in released or main-branch transformers. Do you have a private fork or custom modeling code?"*
 
 - **No `config.json`** → not a standard HF checkpoint. List the directory for README or `.py` files. If nothing useful, ask the user for the modeling code.