Token Classification
Transformers
Safetensors
qwen2
Generated from Trainer
prm
trl
math
process-reward-model
qwen2.5
sharp
text-generation-inference
Instructions to use ZaandaTeika/Qwen2.5-Math-7B-Instruct-SHARP-PRM800K-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZaandaTeika/Qwen2.5-Math-7B-Instruct-SHARP-PRM800K-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ZaandaTeika/Qwen2.5-Math-7B-Instruct-SHARP-PRM800K-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ZaandaTeika/Qwen2.5-Math-7B-Instruct-SHARP-PRM800K-Classifier") model = AutoModelForTokenClassification.from_pretrained("ZaandaTeika/Qwen2.5-Math-7B-Instruct-SHARP-PRM800K-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a65cd02e26c89d7c52ff59f8e170fd7817b17393f9a0c59cd24f42337539653
- Size of remote file:
- 6.1 kB
- SHA256:
- 3b95e9485e91bcf592772337815399efaa24d8b7719aa57dd7d2efdab5d3a8dc
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