sft_concise_qwen25_0.5b
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the concise_sft dataset. It achieves the following results on the evaluation set:
- Loss: 0.6725
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6735 | 0.3592 | 500 | 0.6793 |
| 0.6381 | 0.7184 | 1000 | 0.6572 |
| 0.5229 | 1.0776 | 1500 | 0.6521 |
| 0.521 | 1.4368 | 2000 | 0.6386 |
| 0.5014 | 1.7960 | 2500 | 0.6232 |
| 0.3465 | 2.1552 | 3000 | 0.6751 |
| 0.3495 | 2.5144 | 3500 | 0.6769 |
| 0.3564 | 2.8736 | 4000 | 0.6726 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.4.1+cu124
- Datasets 4.0.0
- Tokenizers 0.22.2
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