Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use bikalnetomi/RLHF-PPO-RewardModel-LLama3-1B-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bikalnetomi/RLHF-PPO-RewardModel-LLama3-1B-v1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bikalnetomi/RLHF-PPO-RewardModel-LLama3-1B-v1.1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bikalnetomi/RLHF-PPO-RewardModel-LLama3-1B-v1.1") model = AutoModelForSequenceClassification.from_pretrained("bikalnetomi/RLHF-PPO-RewardModel-LLama3-1B-v1.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6965569bded16a6530dd92fa6076b74075f87581cdc9ed383b565729c44e9520
- Size of remote file:
- 5.37 kB
- SHA256:
- ccdcbec1233a9f9dc414cdbb696502c39fb6a4b68a755f9a56c7ec217352c42d
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