Instructions to use te-sla/sum600 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use te-sla/sum600 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="te-sla/sum600")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("te-sla/sum600") model = AutoModelForCausalLM.from_pretrained("te-sla/sum600", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff6b336a89c51158184e30c686b59ee882c0e2f8ec0a94d4a72b01d9dce3c861
- Size of remote file:
- 12.1 MB
- SHA256:
- 353af0dcc3bac8567353d9e904662cb279dc364ba2d721ddaa766b1d96df8dee
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