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", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 28f7203e655c12b0a8156d245ee40ceb0c892eb16e29b638cac081fbc089254c
- Size of remote file:
- 345 kB
- SHA256:
- e3f65b8d9cd7ad1d87ef894b722f34b84206e2dce5c40db76c803fec03558aac
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