Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use tsessk/llm-course-hw2-reward-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tsessk/llm-course-hw2-reward-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tsessk/llm-course-hw2-reward-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tsessk/llm-course-hw2-reward-model") model = AutoModelForSequenceClassification.from_pretrained("tsessk/llm-course-hw2-reward-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94f14bcc9cbadcecad4f579b9be81c8dc056f849bc4ff27f0a2384296542e3fb
- Size of remote file:
- 269 MB
- SHA256:
- 66b7c3ca6353ac4dff90d81bdf662c04cc573533918bf6438cc7d10d4c8a5afa
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