Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use pashash0k/reward_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pashash0k/reward_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pashash0k/reward_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pashash0k/reward_model") model = AutoModelForSequenceClassification.from_pretrained("pashash0k/reward_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1703e18874f73144ca1a2dff8fb6f6b4554a1413247b848fdb61ddd3c9c2ca63
- Size of remote file:
- 5.43 kB
- SHA256:
- af345136075668dcc468b869678b735d4e861098ef58fc904077d9b28ed42df4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.