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:
- 2459d43c177896b44c3f69ec589b73a648037d0a7168761868bfc15e13e56dd0
- Size of remote file:
- 11.4 MB
- SHA256:
- 291d05ad00455fef901f3c9cdbd95f896c591cdff089f3189f95d3171634563f
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