Instructions to use PoseyATX/Moist-Pony with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PoseyATX/Moist-Pony with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="PoseyATX/Moist-Pony")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PoseyATX/Moist-Pony") model = AutoModelForSeq2SeqLM.from_pretrained("PoseyATX/Moist-Pony", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from PoseyATX/Moist-Pony: direct link, hf CLI and curl.
- Browser
- Download file 1.22 GB
-
https://huggingface.co/PoseyATX/Moist-Pony/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://PoseyATX/Moist-Pony/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/PoseyATX/Moist-Pony/resolve/main/pytorch_model.bin
1.22 GB
- Xet hash:
- cc8b8ecea856086c779c6357f8976af1c63f045fc54c722db314d3779ba510fa
- Size of remote file:
- 1.22 GB
- SHA256:
- cb90c3685cb4f33398309ab3d3f3b91f402a95f898d7d4a27495e3cf295c667b
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