--- 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 llama3-8b-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 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 | 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 | 74.22 | 36.17 | 15.68 | 20.0 | 12.14 | 69.47 | 34.21 | 14.72 | 20.25 | 14.07 | | llama3-8b-agent-instruct-v2 | **85.15** | **58.1** | **1.57** | **52.10** | **26.02** | **85.79** | **59.43** | **2.56** | **52.19** | **31.43** | For detailed explanations of the evaluation metrics, please refer to [document](https://github.com/modelscope/eval-scope/tree/main/llmuses/third_party/toolbench_static) Deploy this model: ```shell USE_HF=True swift deploy \ --model_id_or_path modelscope/llama3-8b-agent-instruct-v2 \ --model_type llama3-8b-instruct \ --infer_backend vllm \ --tools_prompt toolbench ```