Nlp environment management

Manages NLP environments, handling library dependencies like PyTorch, Transformers, and custom local packages.From its SKILL.md

Install

npx -y skills add cxcscmu/SkillLearnBench --skill nlp-environment-management

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SKILL.md

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Setting up an environment for NLP research requires specific versions of deep learning libraries and often custom local modules.

If an environment.yml is provided:

# Update existing environment
conda env update -n base --file environment.yml

Or create a new one:

conda env create -f environment.yml

Flash Attention: Requires flash-attn and often specific CUDA versions. Install using:
```
pip install flash-attn --no-build-isolation
```
Transformers/TRL Versions: Ensure transformers and trl versions match the codebase's expectations.
Local Modules: If a project uses local modules, ensure they are in the PYTHONPATH:
```
export PYTHONPATH=$PYTHONPATH:$(pwd)
```

Always log the environment for reproducibility:

python -VV > python_info.txt
python -m pip freeze >> python_info.txt

Read from the repository

Just SKILL.md. No reference files, no scripts.