Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use Lexu3A/reward_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lexu3A/reward_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Lexu3A/reward_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Lexu3A/reward_model") model = AutoModelForSequenceClassification.from_pretrained("Lexu3A/reward_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Lexu3A/reward_model: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Lexu3A/reward_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Lexu3A/reward_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Lexu3A/reward_model/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 06bcfc5921fe96dc1f0615c222f9c24c5e28ba28e1fe6dc7a096850f7bee8e90
- Size of remote file:
- 5.37 kB
- SHA256:
- 5e3d71dee32a2cb909dc8951bf96637aaea1ea25638223e811db3076ff4d6ae7
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