---
library_name: transformers
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
tags:
- generated_from_trainer
metrics:
- accuracy
- rouge
model-index:
- name: ddfcd73bbe6123a96753ab5b4f63585e
  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. -->

# ddfcd73bbe6123a96753ab5b4f63585e

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 dim/tldr_news dataset.
It achieves the following results on the evaluation set:
- Loss: 8.8694
- Data Size: 1.0
- Epoch Runtime: 318.5639
- Accuracy: 0.7592
- F1 Macro: 0.7855
- Rouge1: 0.7599
- Rouge2: 0.0
- Rougel: 0.7592
- Rougelsum: 0.7592

## 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    | 14.8617         | 0         | 7.6791        | 0.3011   | 0.1662   | 0.3018 | 0.0    | 0.3011 | 0.3011    |
| No log        | 1     | 178  | 21.2796         | 0.0078    | 9.5885        | 0.2891   | 0.1516   | 0.2898 | 0.0    | 0.2891 | 0.2891    |
| No log        | 2     | 356  | 8.3821          | 0.0156    | 23.9192       | 0.5938   | 0.4083   | 0.5945 | 0.0    | 0.5938 | 0.5945    |
| No log        | 3     | 534  | 5.2765          | 0.0312    | 42.1691       | 0.6271   | 0.4965   | 0.6278 | 0.0    | 0.6271 | 0.6264    |
| No log        | 4     | 712  | 3.3509          | 0.0625    | 67.5970       | 0.6740   | 0.5494   | 0.6747 | 0.0    | 0.6733 | 0.6733    |
| No log        | 5     | 890  | 2.9493          | 0.125     | 91.1434       | 0.7251   | 0.6998   | 0.7251 | 0.0    | 0.7259 | 0.7259    |
| 0.2973        | 6     | 1068 | 2.5491          | 0.25      | 141.9162      | 0.7756   | 0.7789   | 0.7770 | 0.0    | 0.7763 | 0.7763    |
| 2.0618        | 7     | 1246 | 2.4829          | 0.5       | 198.2432      | 0.7841   | 0.8192   | 0.7841 | 0.0    | 0.7841 | 0.7848    |
| 1.3653        | 8.0   | 1424 | 2.7490          | 1.0       | 331.8644      | 0.7557   | 0.7698   | 0.7557 | 0.0    | 0.7557 | 0.7557    |
| 0.5211        | 9.0   | 1602 | 4.4314          | 1.0       | 318.8436      | 0.7621   | 0.8016   | 0.7635 | 0.0    | 0.7621 | 0.7628    |
| 0.4182        | 10.0  | 1780 | 7.0370          | 1.0       | 319.3421      | 0.7422   | 0.7869   | 0.7429 | 0.0    | 0.7422 | 0.7415    |
| 0.3976        | 11.0  | 1958 | 8.8694          | 1.0       | 318.5639      | 0.7592   | 0.7855   | 0.7599 | 0.0    | 0.7592 | 0.7592    |


### Framework versions

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