85 lines
3.3 KiB
Markdown
85 lines
3.3 KiB
Markdown
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---
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frameworks:
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- Pytorch
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license: Apache License 2.0
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tasks:
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- text-generation
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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---
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Fine-tuning the qwen2-7b-instruct model using the [msagent-pro](https://modelscope.cn/datasets/iic/MSAgent-Pro/summary) dataset and the loss_scale technique with [swift](https://github.com/modelscope/swift), the script is as follows:
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```bash
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NPROC_PER_NODE=8 \
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
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MASTER_PORT=29500 \
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swift sft \
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--model_type qwen2-7b-instruct \
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--learning_rate 2e-6 \
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--sft_type full \
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--dataset msagent-pro \
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--gradient_checkpointing true \
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--gradient_accumulation_steps 8 \
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--deepspeed default-zero3 \
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--use_loss_scale true \
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--save_strategy epoch \
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--batch_size 1 \
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--num_train_epochs 1 \
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--max_length 4096 \
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--preprocess_num_proc 4 \
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--use_loss_scale true \
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--loss_scale_config_path agent-flan \
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--ddp_backend nccl \
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```
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Comparison with the Original Model on the ToolBench Evaluation Set
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| Model | ToolBench (in-domain) | | | | | ToolBench (out-of-domain) | | | |
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|-------------------------|----------------------------------------------|-------|-------|-------|-------|--------------------------------------------|-------|-------|-------|
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| | Plan.EM | Act.EM| HalluRate (lower is better) | Avg.F1 | R-L | Plan.EM | Act.EM| HalluRate (lower is better) | Avg.F1 | R-L |
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| qwen2-7b-instruct | 74.11 | 54.74 | 4.16 | 46.53 | 8.51 | 73.17 | 57.67 | 3.84 | 48.58 | 11.23 |
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| qwen2-7b-agent-instruct | **83.37** | **60.01** | **2.58** | **54.41** | **26.34** | **82.57** | **60.14** | **1.79** | **55.25** | **31.34** |
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For detailed explanations of the evaluation metrics, please refer to [document](https://github.com/modelscope/eval-scope/tree/main/llmuses/third_party/toolbench_static)
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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('swift/qwen2-7b-agent-instruct')
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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/swift/qwen2-7b-agent-instruct.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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