Instructions to use AngLv/NoisyRewards-in-RL-RM-acc-65 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AngLv/NoisyRewards-in-RL-RM-acc-65 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AngLv/NoisyRewards-in-RL-RM-acc-65")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AngLv/NoisyRewards-in-RL-RM-acc-65") model = AutoModelForSequenceClassification.from_pretrained("AngLv/NoisyRewards-in-RL-RM-acc-65", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AngLv/NoisyRewards-in-RL-RM-acc-65: direct link, hf CLI and curl.
- Browser
- Download file 409 Bytes
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https://huggingface.co/AngLv/NoisyRewards-in-RL-RM-acc-65/resolve/main/README.md
- Command line
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hf download hf://AngLv/NoisyRewards-in-RL-RM-acc-65/README.md
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curl -L -o README.md https://huggingface.co/AngLv/NoisyRewards-in-RL-RM-acc-65/resolve/main/README.md
409 Bytes
metadata
license: mit
pipeline_tag: text-classification
library_name: transformers
File information
The repository contains the following file information:
The model was presented in the paper The Climb Carves Wisdom Deeper Than the Summit: On the Noisy Rewards in Learning to Reason.
Github code: https://github.com/trestad/Noisy-Rewards-in-Learning-to-Reason