Instructions to use GagaLey/MoR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GagaLey/MoR with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="GagaLey/MoR")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GagaLey/MoR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download Planning/utils.py from GagaLey/MoR: direct link, hf CLI and curl.
- Browser
- Download file 208 Bytes
-
https://huggingface.co/GagaLey/MoR/resolve/main/Planning/utils.py
- Command line
-
hf download hf://GagaLey/MoR/Planning/utils.py
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curl -L -o utils.py https://huggingface.co/GagaLey/MoR/resolve/main/Planning/utils.py
208 Bytes
| def remove_inner_single_quotes(match): | |
| content = match.group(1) # Get the string inside [''] | |
| cleaned_content = content.replace("'", "") # Remove inner single quotes | |
| return f"['{cleaned_content}" |