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---
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
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