---
library_name: transformers
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
tags:
- generated_from_trainer
model-index:
- name: genrm-deepseek-ai-DeepSeek-R1-Distill-Qwen-14B
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# genrm-deepseek-ai-DeepSeek-R1-Distill-Qwen-14B

This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2953

## 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: 3e-07
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_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: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.2028        | 0.0163 | 200   | 0.3168          |
| 0.1841        | 0.0326 | 400   | 0.3174          |
| 0.178         | 0.0489 | 600   | 0.3028          |
| 0.193         | 0.0652 | 800   | 0.3068          |
| 0.1912        | 0.0815 | 1000  | 0.3044          |
| 0.189         | 0.0979 | 1200  | 0.3004          |
| 0.1857        | 0.1142 | 1400  | 0.3047          |
| 0.175         | 0.1305 | 1600  | 0.3043          |
| 0.1717        | 0.1468 | 1800  | 0.2981          |
| 0.1779        | 0.1631 | 2000  | 0.3049          |
| 0.1727        | 0.1794 | 2200  | 0.3041          |
| 0.1796        | 0.1957 | 2400  | 0.3054          |
| 0.1843        | 0.2120 | 2600  | 0.2994          |
| 0.1832        | 0.2283 | 2800  | 0.3020          |
| 0.1642        | 0.2446 | 3000  | 0.3049          |
| 0.1954        | 0.2609 | 3200  | 0.2988          |
| 0.1884        | 0.2773 | 3400  | 0.2992          |
| 0.1717        | 0.2936 | 3600  | 0.2983          |
| 0.1744        | 0.3099 | 3800  | 0.2982          |
| 0.1717        | 0.3262 | 4000  | 0.3021          |
| 0.1519        | 0.3425 | 4200  | 0.2989          |
| 0.1857        | 0.3588 | 4400  | 0.2981          |
| 0.1802        | 0.3751 | 4600  | 0.3004          |
| 0.1637        | 0.3914 | 4800  | 0.2981          |
| 0.1611        | 0.4077 | 5000  | 0.2993          |
| 0.1957        | 0.4240 | 5200  | 0.2973          |
| 0.169         | 0.4403 | 5400  | 0.2950          |
| 0.1542        | 0.4567 | 5600  | 0.2972          |
| 0.1669        | 0.4730 | 5800  | 0.2943          |
| 0.1667        | 0.4893 | 6000  | 0.2944          |
| 0.1742        | 0.5056 | 6200  | 0.2963          |
| 0.1676        | 0.5219 | 6400  | 0.2951          |
| 0.1537        | 0.5382 | 6600  | 0.2975          |
| 0.1876        | 0.5545 | 6800  | 0.2979          |
| 0.1543        | 0.5708 | 7000  | 0.2980          |
| 0.1709        | 0.5871 | 7200  | 0.2981          |
| 0.1608        | 0.6034 | 7400  | 0.2967          |
| 0.1727        | 0.6198 | 7600  | 0.2973          |
| 0.1659        | 0.6361 | 7800  | 0.2959          |
| 0.1862        | 0.6524 | 8000  | 0.2990          |
| 0.1758        | 0.6687 | 8200  | 0.2974          |
| 0.1981        | 0.6850 | 8400  | 0.2965          |
| 0.1616        | 0.7013 | 8600  | 0.2943          |
| 0.1738        | 0.7176 | 8800  | 0.2943          |
| 0.1775        | 0.7339 | 9000  | 0.2958          |
| 0.1683        | 0.7502 | 9200  | 0.2949          |
| 0.1632        | 0.7665 | 9400  | 0.2946          |
| 0.1702        | 0.7828 | 9600  | 0.2937          |
| 0.1937        | 0.7992 | 9800  | 0.2944          |
| 0.162         | 0.8155 | 10000 | 0.2964          |
| 0.167         | 0.8318 | 10200 | 0.2968          |
| 0.1708        | 0.8481 | 10400 | 0.2963          |
| 0.16          | 0.8644 | 10600 | 0.2970          |
| 0.1695        | 0.8807 | 10800 | 0.2944          |
| 0.1568        | 0.8970 | 11000 | 0.2948          |
| 0.1708        | 0.9133 | 11200 | 0.2952          |
| 0.1561        | 0.9296 | 11400 | 0.2961          |
| 0.158         | 0.9459 | 11600 | 0.2950          |
| 0.1763        | 0.9622 | 11800 | 0.2948          |
| 0.1579        | 0.9786 | 12000 | 0.2950          |
| 0.1512        | 0.9949 | 12200 | 0.2953          |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3