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qwen2-7b-agent-instruct

Text Generation
Transformers
Safetensors
qwen2
conversational
text-generation-inference
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qwen2-7b-agent-instruct
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---
frameworks:
- Pytorch
license: apache-2.0
tasks:
- text-generation

#model-type:
##如 gpt、phi、llama、chatglm、baichuan 等
#- gpt

#domain:
##如 nlp、cv、audio、multi-modal
#- nlp

#language:
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
#- cn 

#metrics:
##如 CIDEr、Blue、ROUGE 等
#- CIDEr

#tags:
##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
#- pretrained

#tools:
##如 vllm、fastchat、llamacpp、AdaSeq 等
#- vllm
---

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:
```bash
NPROC_PER_NODE=8 \
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
MASTER_PORT=29500 \
swift sft \
    --model_type qwen2-7b-instruct \
    --learning_rate 2e-6 \
    --sft_type full \
    --dataset msagent-pro \
    --gradient_checkpointing true \
    --gradient_accumulation_steps 8 \
    --deepspeed default-zero3 \
    --use_loss_scale true \
    --save_strategy epoch \
    --batch_size 1 \
    --num_train_epochs 1 \
    --max_length 4096 \
    --preprocess_num_proc 4 \
    --use_loss_scale true \
    --loss_scale_config_path agent-flan \
    --ddp_backend nccl \
```

Comparison with the Original Model on the ToolBench Evaluation Set

| Model                   | ToolBench (in-domain)                        |       |       |       |       | ToolBench (out-of-domain)                  |       |       |       |
|-------------------------|----------------------------------------------|-------|-------|-------|-------|--------------------------------------------|-------|-------|-------|
|                         | Plan.EM                                      | Act.EM| HalluRate (lower is better) | Avg.F1 | R-L   | Plan.EM                                   | Act.EM| HalluRate (lower is better) | Avg.F1 | R-L   |
| llama3-8b-instruct      | 74.11                                       | 54.74 | 4.16                     | 46.53  | 8.51 | 73.17                                     | 57.67 | 3.84                  | 48.58  | 11.23 |
| llama3-8b-agent-instruct-v2   | **83.37**                                        | **60.01** | **2.58**                     | **54.41** | **26.34** | **82.57**                                     | **60.14** | **1.79**                  | **55.25**  | **31.34** |

For detailed explanations of the evaluation metrics, please refer to [document](https://github.com/modelscope/eval-scope/tree/main/llmuses/third_party/toolbench_static)