Instructions to use RLHFlow/RewardModel-Mistral-7B-for-DPA-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RLHFlow/RewardModel-Mistral-7B-for-DPA-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RLHFlow/RewardModel-Mistral-7B-for-DPA-v1", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RLHFlow/RewardModel-Mistral-7B-for-DPA-v1", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("RLHFlow/RewardModel-Mistral-7B-for-DPA-v1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 975e2dddf6a906c5dca403ab805aed2314b7f21ecf6145f88111236f1b46860f
- Size of remote file:
- 4.94 GB
- SHA256:
- 85b6ef24ca5fe79ade30cbb9dd8d714dbc67f37c7021d57de0c32b889f1cf5e1
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