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/checkpoints/amazon/lora_model/tokenizer.json from GagaLey/MoR: direct link, hf CLI and curl.
- Browser
- Download file 9.09 MB
-
https://huggingface.co/GagaLey/MoR/resolve/main/Planning/checkpoints/amazon/lora_model/tokenizer.json
- Command line
-
hf download hf://GagaLey/MoR/Planning/checkpoints/amazon/lora_model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/GagaLey/MoR/resolve/main/Planning/checkpoints/amazon/lora_model/tokenizer.json
9.09 MB
- Xet hash:
- 5cdea387e5b8478fd9d75abfa38e9ec5fdd6c2bf542a433c3cb4e487d5d44323
- Size of remote file:
- 9.09 MB
- SHA256:
- 79e3e522635f3171300913bb421464a87de6222182a0570b9b2ccba2a964b2b4
路
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