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
Update config.json
Browse files- config.json +1 -1
config.json
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{
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"_name_or_path": "
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"architectures": [
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"MistralForAttributePrediction"
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],
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{
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"_name_or_path": "RLHFlow/RewardModel-Mistral-7B-for-DPA-v1",
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"architectures": [
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"MistralForAttributePrediction"
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],
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