Text Classification
Transformers
Safetensors
English
qwen2
reward-model
3b
RLHF
text-embeddings-inference
Instructions to use kanishkez/Reward-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kanishkez/Reward-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kanishkez/Reward-Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kanishkez/Reward-Model") model = AutoModelForSequenceClassification.from_pretrained("kanishkez/Reward-Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05d2240f61e4f9ea641b955562ce423ed4b1de023260656aaf474d46d31d2a06
- Size of remote file:
- 7.03 MB
- SHA256:
- c0382117ea329cdf097041132f6d735924b697924d6f6fc3945713e96ce87539
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.