---
library_name: transformers
base_model: PowerInfer/SmallThinker-3B-Preview
tags:
- generated_from_trainer
model-index:
- name: powerinfer-seq-cls-ywko_e5
  results: []
---

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# powerinfer-seq-cls-ywko_e5

This model is a fine-tuned version of [PowerInfer/SmallThinker-3B-Preview](https://huggingface.co/PowerInfer/SmallThinker-3B-Preview) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0727

## 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: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 4096
- 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_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4779        | 0.9143 | 30   | 0.1895          |
| 0.1332        | 1.8533 | 60   | 0.1015          |
| 0.0797        | 2.7924 | 90   | 0.0780          |
| 0.0811        | 3.7314 | 120  | 0.0743          |
| 0.0669        | 4.6705 | 150  | 0.0727          |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1