Token Classification
Transformers
Safetensors
qwen2
Generated from Trainer
trl
prm
text-generation-inference
Instructions to use hzy/Qwen2.5-Math-7B-Instruct-PRM-Modified-math_shepherd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hzy/Qwen2.5-Math-7B-Instruct-PRM-Modified-math_shepherd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hzy/Qwen2.5-Math-7B-Instruct-PRM-Modified-math_shepherd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hzy/Qwen2.5-Math-7B-Instruct-PRM-Modified-math_shepherd") model = AutoModelForTokenClassification.from_pretrained("hzy/Qwen2.5-Math-7B-Instruct-PRM-Modified-math_shepherd") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- all_results.json +8 -0
- eval_results.json +8 -0
all_results.json
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{
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"epoch": 0.9998485078018482,
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"eval_accuracy": 0.8723179132087151,
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"eval_loss": 0.2878721058368683,
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"eval_runtime": 851.7178,
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"eval_samples_per_second": 26.103,
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"eval_steps_per_second": 1.632
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}
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eval_results.json
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{
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"epoch": 0.9998485078018482,
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"eval_accuracy": 0.8723179132087151,
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"eval_loss": 0.2878721058368683,
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"eval_runtime": 851.7178,
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"eval_samples_per_second": 26.103,
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"eval_steps_per_second": 1.632
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}
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