Text Classification
Transformers
TensorBoard
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use SINGHANKIT/Reward_MedLlama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SINGHANKIT/Reward_MedLlama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SINGHANKIT/Reward_MedLlama")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SINGHANKIT/Reward_MedLlama") model = AutoModelForSequenceClassification.from_pretrained("SINGHANKIT/Reward_MedLlama") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2387e1db8c56dee256219001bb15d8014a8dbc21fcb53199dc4d416a14a84a71
- Size of remote file:
- 17.2 MB
- SHA256:
- 76cfe2f054560aae896b2b75e273dc97a39e304d4ad19c44a9727a1d6b33c4cc
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