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:
- aa1e038bc132269a1e26be54d7fcd187462c7e15c44317db2c3aac58fc7a8f36
- Size of remote file:
- 5 GB
- SHA256:
- 49fbbc1906d631c3da0ee6eb7f4778d106b60a840b7453146a69f5fc38a76aec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.