Text Classification
Transformers
Safetensors
English
qwen2
text-generation
math
reasoning
text-embeddings-inference
Instructions to use declare-lab/PathFinder-PRM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use declare-lab/PathFinder-PRM-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="declare-lab/PathFinder-PRM-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("declare-lab/PathFinder-PRM-7B") model = AutoModelForCausalLM.from_pretrained("declare-lab/PathFinder-PRM-7B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8005a4ec76c871c1fa279c59c6b342214a8b5fc6651347b45c1cab6b70e0659
- Size of remote file:
- 6.71 kB
- SHA256:
- 22f9ce7a88c83e56706349177602a9159f64407953ba60193107c05d465599cd
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