metadata
frameworks:
- Pytorch
license: apache-2.0
tasks:
- text-generation
Fine-tuning the qwen2-7b-instruct model using the msagent-pro dataset and the loss_scale technique with swift, the script is as follows:
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 | |
| llama3-8b-instruct | 74.11 | 54.74 | 4.16 | 46.53 | 8.51 | 73.17 | 57.67 | 3.84 | 48.58 |
| llama3-8b-agent-instruct-v2 | 83.37 | 60.01 | 2.58 | 54.41 | 26.34 | 82.57 | 60.14 | 1.79 | 55.25 |
For detailed explanations of the evaluation metrics, please refer to document