Instructions to use spursyy/mt5-small-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spursyy/mt5-small-2 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="spursyy/mt5-small-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("spursyy/mt5-small-2") model = AutoModelForSeq2SeqLM.from_pretrained("spursyy/mt5-small-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from spursyy/mt5-small-2: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/spursyy/mt5-small-2/resolve/main/training_args.bin
- Command line
-
hf download hf://spursyy/mt5-small-2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/spursyy/mt5-small-2/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- 30640c8ee537148bb1c741da28c9d9d775979d5aaf6b78aa30b10465c9c52575
- Size of remote file:
- 3.18 kB
- SHA256:
- aa4a51ab894986324056067d4cff910564c0db59adfa272698be297ac1831722
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