---
license: Apache License 2.0
language:
  - zh
tasks:
  - text-generation
base_model:
  - Qwen/Qwen2.5-7B-Instruct
base_model_relation: finetune
tags:
  - lora
  - llama-factory
---
<!-- 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. -->

# qwen2.5-75b-lora
该模型是在法律案件数据集上对 ./Qwen/Qwen2.5-7B-Instruct 进行微调后的版本。

- Loss： 0.4874
- eval_loss：0.48537


## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

### Training hyperparameters
#### train
- per_device_train_batch_size: 8
- gradient_accumulation_steps: 4
- learning_rate: 2.0e-4
- num_train_epochs: 2.0
- lr_scheduler_type: cosine
- warmup_ratio: 0.1
- bf16: true
- ddp_timeout: 180000000
- resume_from_checkpoint: null

#### eval
- eval_dataset: my_dataset_test
- per_device_eval_batch_size: 4
- eval_strategy: steps
- eval_steps: 500  # 调小评估步数，确保训练中执行多次评估
- metric_for_best_model: eval_loss  # 指定核心评估指标
- load_best_model_at_end: true  # 训练结束后加载最优模型

### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:------------ |:---- |:--- |:-------------- |
| 0.8000        | 0.45  | 500  | 0.5190          |
| 0.4995        | 0.90  | 1000 | 0.4995          |
| 0.4874        | 1.36  | 1500 | 0.4910          |
| 0.4121        | 1.81  | 2000 | 0.4854          |
| 0.4874        | 2.00  | 2212 | 0.4854          |


---
### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型


SDK下载
```bash
#安装ModelScope
pip install modelscope
```
```python
#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('tongzang/Qwen2.5-7b-lora-law')
```
Git下载
```
#Git模型下载
git clone https://www.modelscope.cn/tongzang/Qwen2.5-7b-lora-law.git
```

<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>