---
library_name: transformers
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
tags:
- generated_from_trainer
metrics:
- accuracy
- rouge
model-index:
- name: 43bd7e74faf21e9f15deaaad494b15d5
  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. -->

# 43bd7e74faf21e9f15deaaad494b15d5

This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) on the nyu-mll/glue [mrpc] dataset.
It achieves the following results on the evaluation set:
- Loss: 5.8488
- Data Size: 1.0
- Epoch Runtime: 120.6807
- Accuracy: 0.7995
- F1 Macro: 0.7842
- Rouge1: 0.7995
- Rouge2: 0.0
- Rougel: 0.7989
- Rougelsum: 0.7995

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Data Size | Epoch Runtime | Accuracy | F1 Macro | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------------:|:--------:|:--------:|:------:|:------:|:------:|:---------:|
| No log        | 0     | 0    | 7.4657          | 0         | 5.7654        | 0.6604   | 0.4107   | 0.6616 | 0.0    | 0.6604 | 0.6604    |
| No log        | 1     | 114  | 79.7619         | 0.0078    | 6.1570        | 0.3349   | 0.2509   | 0.3343 | 0.0    | 0.3355 | 0.3349    |
| No log        | 2     | 228  | 37.1240         | 0.0156    | 18.8215       | 0.3349   | 0.2509   | 0.3343 | 0.0    | 0.3355 | 0.3349    |
| No log        | 3     | 342  | 5.1122          | 0.0312    | 30.8926       | 0.6792   | 0.4787   | 0.6792 | 0.0    | 0.6787 | 0.6787    |
| 0.4304        | 4     | 456  | 3.2691          | 0.0625    | 39.4693       | 0.6462   | 0.6327   | 0.6462 | 0.0    | 0.6456 | 0.6468    |
| 0.4304        | 5     | 570  | 1.8845          | 0.125     | 48.9782       | 0.7983   | 0.7760   | 0.7983 | 0.0    | 0.7983 | 0.7983    |
| 0.4304        | 6     | 684  | 2.8282          | 0.25      | 60.8006       | 0.6291   | 0.6291   | 0.6285 | 0.0    | 0.6285 | 0.6279    |
| 0.5711        | 7     | 798  | 1.5321          | 0.5       | 75.4529       | 0.8296   | 0.8123   | 0.8296 | 0.0    | 0.8290 | 0.8296    |
| 1.0265        | 8.0   | 912  | 1.8242          | 1.0       | 119.6966      | 0.8031   | 0.7856   | 0.8031 | 0.0    | 0.8037 | 0.8025    |
| 0.4925        | 9.0   | 1026 | 3.1007          | 1.0       | 117.1617      | 0.7930   | 0.7325   | 0.7930 | 0.0    | 0.7933 | 0.7925    |
| 0.3132        | 10.0  | 1140 | 3.5572          | 1.0       | 129.2212      | 0.8308   | 0.8116   | 0.8308 | 0.0    | 0.8308 | 0.8302    |
| 0.3243        | 11.0  | 1254 | 5.8488          | 1.0       | 120.6807      | 0.7995   | 0.7842   | 0.7995 | 0.0    | 0.7989 | 0.7995    |


### Framework versions

- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.1