Token Classification
Transformers
Safetensors
qwen2
Generated from Trainer
trl
stepwise-reward-trainer
text-generation-inference
Instructions to use qgallouedec/Qwen2-0.5B-Reward-Math-Sheperd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qgallouedec/Qwen2-0.5B-Reward-Math-Sheperd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="qgallouedec/Qwen2-0.5B-Reward-Math-Sheperd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("qgallouedec/Qwen2-0.5B-Reward-Math-Sheperd") model = AutoModelForTokenClassification.from_pretrained("qgallouedec/Qwen2-0.5B-Reward-Math-Sheperd") - 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": 1.0,
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"eval_accuracy": 0.9966289556146843,
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"eval_loss": 0.007473934907466173,
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"eval_runtime": 63.1801,
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"eval_samples_per_second": 351.899,
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"eval_steps_per_second": 5.508
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}
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eval_results.json
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{
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"epoch": 1.0,
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"eval_accuracy": 0.9966289556146843,
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"eval_loss": 0.007473934907466173,
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"eval_runtime": 63.1801,
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"eval_samples_per_second": 351.899,
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"eval_steps_per_second": 5.508
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}
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