Instructions to use jojo2joker/MATH_training_split_distill_large_teacher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jojo2joker/MATH_training_split_distill_large_teacher with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jojo2joker/MATH_training_split_distill_large_teacher", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:Config file config.json cannot be fetched (too big)
Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)
MATH_training_split_distill_large_teacher
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the MATH_training_split_distill_large_teacher dataset. It achieves the following results on the evaluation set:
- Loss: 0.1529
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1932 | 0.1781 | 100 | 0.1786 |
| 0.201 | 0.3561 | 200 | 0.1736 |
| 0.1219 | 0.5342 | 300 | 0.1718 |
| 0.1642 | 0.7122 | 400 | 0.1627 |
| 0.1437 | 0.8903 | 500 | 0.1561 |
| 0.0678 | 1.0677 | 600 | 0.1595 |
| 0.0657 | 1.2457 | 700 | 0.1616 |
| 0.0602 | 1.4238 | 800 | 0.1589 |
| 0.0591 | 1.6018 | 900 | 0.1550 |
| 0.0572 | 1.7799 | 1000 | 0.1529 |
| 0.0592 | 1.9579 | 1100 | 0.1528 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.7.0+cu126
- Datasets 3.2.0
- Tokenizers 0.21.0
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