llama3-8b-智能体Agent模型V2版本

Fine-tuning the llama3-8b-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 llama3-8b-instruct \
    --learning_rate 2e-5 \
    --sft_type lora \
    --dataset msagent-pro \
    --gradient_checkpointing true \
    --gradient_accumulation_steps 8 \
    --deepspeed default-zero3 \
    --lora_target_modules ALL \
    --use_loss_scale true \
    --save_strategy epoch \
    --batch_size 1 \
    --num_train_epochs 2 \
    --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

For detailed explanations of the evaluation metrics, please refer to document

您可以通过如下git clone命令，或者ModelScope SDK来下载模型

SDK下载

#安装ModelScope
pip install modelscope

#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('swift/llama3-8b-agent-instruct-v2')

Git下载

#Git模型下载
git clone https://www.modelscope.cn/swift/llama3-8b-agent-instruct-v2.git

如果您是本模型的贡献者，我们邀请您根据模型贡献文档，及时完善模型卡片内容。