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Model: tongzang/Qwen2.5-7b-lora-law Source: Original Platform
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README.md
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README.md
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---
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license: Apache License 2.0
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language:
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- zh
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tasks:
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- text-generation
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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base_model_relation: finetune
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tags:
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- lora
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- llama-factory
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# qwen2.5-75b-lora
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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。
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- Loss: 0.4874
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- eval_loss:0.48537
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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### Training hyperparameters
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#### train
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- per_device_train_batch_size: 8
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- gradient_accumulation_steps: 4
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- learning_rate: 2.0e-4
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- num_train_epochs: 2.0
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- lr_scheduler_type: cosine
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- warmup_ratio: 0.1
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- bf16: true
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- ddp_timeout: 180000000
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- resume_from_checkpoint: null
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#### eval
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- eval_dataset: my_dataset_test
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- per_device_eval_batch_size: 4
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- eval_strategy: steps
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- eval_steps: 500 # 调小评估步数,确保训练中执行多次评估
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- metric_for_best_model: eval_loss # 指定核心评估指标
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- load_best_model_at_end: true # 训练结束后加载最优模型
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:------------ |:---- |:--- |:-------------- |
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| 0.8000 | 0.45 | 500 | 0.5190 |
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| 0.4995 | 0.90 | 1000 | 0.4995 |
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| 0.4874 | 1.36 | 1500 | 0.4910 |
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| 0.4121 | 1.81 | 2000 | 0.4854 |
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| 0.4874 | 2.00 | 2212 | 0.4854 |
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---
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### 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。
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#### 您可以通过如下git clone命令,或者ModelScope SDK来下载模型
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('tongzang/Qwen2.5-7b-lora-law')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/tongzang/Qwen2.5-7b-lora-law.git
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```
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<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>
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