Instructions to use llm-wizard/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-wizard/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="llm-wizard/test_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("llm-wizard/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("llm-wizard/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f18b84fadd8c9ef324b051cf01a13a8525dfada0e9a3982d04b0d2386fa30520
- Size of remote file:
- 433 MB
- SHA256:
- a94f66e8ff3bc22aa2f517864c048958d9efb04fa6c34ae1d72d1f0add51e6fa
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