---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-32B
tags:
- generated_from_trainer
model-index:
- name: Qwen3-32B-3d-1M-100K-0.1-reverse-padzero-plus-mul-sub-99-256D-1L-8H-1024I
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Qwen3-32B-3d-1M-100K-0.1-reverse-padzero-plus-mul-sub-99-256D-1L-8H-1024I

This model is a fine-tuned version of [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1678

## 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: 0.001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- 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: 0.05
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| No log        | 0      | 0     | 3.0430          |
| 1.7196        | 0.0640 | 500   | 1.7064          |
| 1.5254        | 0.1280 | 1000  | 1.5199          |
| 1.3994        | 0.1920 | 1500  | 1.3973          |
| 1.3769        | 0.2560 | 2000  | 1.3764          |
| 1.3556        | 0.3200 | 2500  | 1.3522          |
| 1.3437        | 0.3840 | 3000  | 1.3415          |
| 1.3375        | 0.4480 | 3500  | 1.3353          |
| 1.3328        | 0.5120 | 4000  | 1.3323          |
| 1.3301        | 0.5760 | 4500  | 1.3298          |
| 1.2588        | 0.6400 | 5000  | 1.2525          |
| 1.2274        | 0.7040 | 5500  | 1.2279          |
| 1.2205        | 0.7680 | 6000  | 1.2160          |
| 1.2096        | 0.8319 | 6500  | 1.2098          |
| 1.2084        | 0.8959 | 7000  | 1.2069          |
| 1.2016        | 0.9599 | 7500  | 1.2026          |
| 1.1978        | 1.0239 | 8000  | 1.1980          |
| 1.1931        | 1.0879 | 8500  | 1.1944          |
| 1.1896        | 1.1519 | 9000  | 1.1900          |
| 1.1871        | 1.2159 | 9500  | 1.1862          |
| 1.184         | 1.2799 | 10000 | 1.1851          |
| 1.1822        | 1.3439 | 10500 | 1.1822          |
| 1.1804        | 1.4079 | 11000 | 1.1802          |
| 1.1795        | 1.4719 | 11500 | 1.1788          |
| 1.1792        | 1.5359 | 12000 | 1.1790          |
| 1.1774        | 1.5999 | 12500 | 1.1772          |
| 1.177         | 1.6639 | 13000 | 1.1763          |
| 1.1762        | 1.7279 | 13500 | 1.1756          |
| 1.1745        | 1.7919 | 14000 | 1.1747          |
| 1.173         | 1.8559 | 14500 | 1.1748          |
| 1.176         | 1.9199 | 15000 | 1.1745          |
| 1.1749        | 1.9839 | 15500 | 1.1737          |
| 1.1735        | 2.0479 | 16000 | 1.1741          |
| 1.1719        | 2.1119 | 16500 | 1.1726          |
| 1.1723        | 2.1759 | 17000 | 1.1721          |
| 1.1705        | 2.2399 | 17500 | 1.1716          |
| 1.1704        | 2.3039 | 18000 | 1.1713          |
| 1.1722        | 2.3678 | 18500 | 1.1719          |
| 1.17          | 2.4318 | 19000 | 1.1706          |
| 1.1707        | 2.4958 | 19500 | 1.1707          |
| 1.1713        | 2.5598 | 20000 | 1.1705          |
| 1.1726        | 2.6238 | 20500 | 1.1706          |
| 1.1687        | 2.6878 | 21000 | 1.1699          |
| 1.1705        | 2.7518 | 21500 | 1.1696          |
| 1.1696        | 2.8158 | 22000 | 1.1699          |
| 1.1699        | 2.8798 | 22500 | 1.1694          |
| 1.1691        | 2.9438 | 23000 | 1.1691          |
| 1.1686        | 3.0078 | 23500 | 1.1692          |
| 1.1697        | 3.0718 | 24000 | 1.1693          |
| 1.1692        | 3.1358 | 24500 | 1.1688          |
| 1.1683        | 3.1998 | 25000 | 1.1685          |
| 1.1675        | 3.2638 | 25500 | 1.1685          |
| 1.1693        | 3.3278 | 26000 | 1.1684          |
| 1.1692        | 3.3918 | 26500 | 1.1683          |
| 1.1671        | 3.4558 | 27000 | 1.1682          |
| 1.1686        | 3.5198 | 27500 | 1.1682          |
| 1.1686        | 3.5838 | 28000 | 1.1681          |
| 1.1679        | 3.6478 | 28500 | 1.1680          |
| 1.1683        | 3.7118 | 29000 | 1.1681          |
| 1.1665        | 3.7758 | 29500 | 1.1679          |
| 1.1667        | 3.8398 | 30000 | 1.1679          |
| 1.1674        | 3.9038 | 30500 | 1.1679          |
| 1.1683        | 3.9677 | 31000 | 1.1678          |
| 1.1683        | 4.0317 | 31500 | 1.1678          |
| 1.1689        | 4.0957 | 32000 | 1.1678          |
| 1.1667        | 4.1597 | 32500 | 1.1678          |
| 1.1685        | 4.2237 | 33000 | 1.1678          |
| 1.1689        | 4.2877 | 33500 | 1.1678          |
| 1.1685        | 4.3517 | 34000 | 1.1678          |
| 1.1669        | 4.4157 | 34500 | 1.1678          |
| 1.1675        | 4.4797 | 35000 | 1.1678          |
| 1.1695        | 4.5437 | 35500 | 1.1678          |
| 1.1667        | 4.6077 | 36000 | 1.1678          |
| 1.1667        | 4.6717 | 36500 | 1.1678          |
| 1.1679        | 4.7357 | 37000 | 1.1678          |
| 1.1676        | 4.7997 | 37500 | 1.1678          |
| 1.1689        | 4.8637 | 38000 | 1.1678          |
| 1.1673        | 4.9277 | 38500 | 1.1678          |
| 1.1663        | 4.9917 | 39000 | 1.1678          |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.1