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")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("thnhan3/sft_model") model = AutoModelForSeq2SeqLM.from_pretrained("thnhan3/sft_model") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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max_length=1280,
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truncation=True,
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return_tensors="pt"
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outputs = model.generate(
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inputs.input_ids,
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max_length=1280,
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truncation=True,
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return_tensors="pt"
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).to("cuda")
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outputs = model.generate(
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inputs.input_ids,
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