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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kanishkez/Reward-Model") model = AutoModelForSequenceClassification.from_pretrained("kanishkez/Reward-Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
Verified signature9f4ce97 -
Update README.md
Verified signature4d48fff -
Upload tokenizer.json
Verified signature943dc18 -
Delete tokenizer.json
Verified signature390f408 -
Create config.json
Verified signaturef3b2085 -
Delete config.json
Verified signaturef7cc2ee -
Create tokenizer_config.json
Verified signature1671986 -
Create tokenizer.json
Verified signature386a014 -
Delete tokenizer.json
Verified signature62ce167 -
Delete tokenizer_config.json
Verified signature7d97ac5 -
Update README.md
Verified signature97a8511 -
Update README.md
Verified signature02327d0 -
add model
rootb9776e8 -
initial model
root19ccc63
-
initial commit
Unverified signature593aa19