Instructions to use thnhan3/sft_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thnhan3/sft_model 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="thnhan3/sft_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("thnhan3/sft_model") model = AutoModelForSeq2SeqLM.from_pretrained("thnhan3/sft_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from thnhan3/sft_model: direct link, hf CLI and curl.
- Browser
- Download file 820 kB
-
https://huggingface.co/thnhan3/sft_model/resolve/main/spiece.model
- Command line
-
hf download hf://thnhan3/sft_model/spiece.model
-
curl -L -o spiece.model https://huggingface.co/thnhan3/sft_model/resolve/main/spiece.model
820 kB
- Xet hash:
- bb0d3d5a000bebd114c8f0a77a8cf86ed5c7f6a5f02fd93923b1b5addf2af1d0
- Size of remote file:
- 820 kB
- SHA256:
- 59986b62f9f0b90edafb9b073ea7b93d21114a5841219a1ea2399ade73f729c6
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