Instructions to use mukaj/deepseek-math-7b-rl-prm-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mukaj/deepseek-math-7b-rl-prm-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mukaj/deepseek-math-7b-rl-prm-v0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mukaj/deepseek-math-7b-rl-prm-v0.1") model = AutoModelForSequenceClassification.from_pretrained("mukaj/deepseek-math-7b-rl-prm-v0.1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1297593c62dad223e41879b0d221ec01d5feaa4843bce48b5b150c5e2497e5c0
- Size of remote file:
- 4.99 GB
- SHA256:
- c33d44c9f7a8ab23a264d0a8a9237c251359840d319e50123d4d51fd9c77f44f
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