Instructions to use aloeme/llm-course-hw2-reward-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloeme/llm-course-hw2-reward-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aloeme/llm-course-hw2-reward-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aloeme/llm-course-hw2-reward-model") model = AutoModelForSequenceClassification.from_pretrained("aloeme/llm-course-hw2-reward-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba90fbec5a57d91ae683315d62216d9a2c9b29c731585b2d44eeaf3c284b4426
- Size of remote file:
- 538 MB
- SHA256:
- cfc357c00746b793213586c6432a241559c65018a5c7256ef5f0bd6c216a3183
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