Instructions to use nlpproj2026/rm_opt1.3b_hh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpproj2026/rm_opt1.3b_hh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nlpproj2026/rm_opt1.3b_hh")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nlpproj2026/rm_opt1.3b_hh") model = AutoModelForSequenceClassification.from_pretrained("nlpproj2026/rm_opt1.3b_hh") - Notebooks
- Google Colab
- Kaggle
Model Card for rm_opt1.3b_hh
This model is a fine-tuned version of facebook/opt-1.3b. It has been trained using TRL.
Quick start
from transformers import pipeline
text = "The capital of France is Paris."
rewarder = pipeline(model="None", device="cuda")
output = rewarder(text)[0]
print(output["score"])
Training procedure
This model was trained with Reward.
Framework versions
- TRL: 1.5.1
- Transformers: 5.9.0
- Pytorch: 2.12.0
- Datasets: 4.8.5
- Tokenizers: 0.22.2
Citations
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}
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Model tree for nlpproj2026/rm_opt1.3b_hh
Base model
facebook/opt-1.3b