使用Qwen2___5-Coder-7B-Instruct进行微调，参数如下，出现如下报错，求助求助！！！！ #293
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把data_process 里的data_utils.py 里的895行注释掉 dataset = load_dataset( |
你好，我看config.py里只支持这些模型,使用Qwen2___5-Coder-7B-Instruct是自己对代码修改了以适配该模型吗 SUPPORTED_MODELS = {"LLaMA-7B": "huggyllama/llama-7b","LLaMA-13B": "huggyllama/llama-13b","LLaMA-30B": "huggyllama/llama-30b","LLaMA-65B": "huggyllama/llama-65b","LLaMA2-7B": "meta-llama/Llama-2-7b-hf","LLaMA2-13B": "meta-llama/Llama-2-13b-hf","LLaMA2-70B": "meta-llama/Llama-2-70b-hf","LLaMA2-7B-Chat": "meta-llama/Llama-2-7b-chat-hf","LLaMA2-13B-Chat": "meta-llama/Llama-2-13b-chat-hf","LLaMA2-70B-Chat": "meta-llama/Llama-2-70b-chat-hf","ChineseLLaMA2-7B": "ziqingyang/chinese-llama-2-7b","ChineseLLaMA2-13B": "ziqingyang/chinese-llama-2-13b","ChineseLLaMA2-7B-Chat": "ziqingyang/chinese-alpaca-2-7b","ChineseLLaMA2-13B-Chat": "ziqingyang/chinese-alpaca-2-13b","BLOOM-560M": "bigscience/bloom-560m","BLOOM-3B": "bigscience/bloom-3b","BLOOM-7B1": "bigscience/bloom-7b1","BLOOMZ-560M": "bigscience/bloomz-560m","BLOOMZ-3B": "bigscience/bloomz-3b","BLOOMZ-7B1-mt": "bigscience/bloomz-7b1-mt","Falcon-7B": "tiiuae/falcon-7b","Falcon-7B-Chat": "tiiuae/falcon-7b-instruct","Falcon-40B": "tiiuae/falcon-40b","Falcon-40B-Chat": "tiiuae/falcon-40b-instruct","Baichuan-7B": "baichuan-inc/Baichuan-7B","Baichuan-13B": "baichuan-inc/Baichuan-13B-Base","Baichuan-13B-Chat": "baichuan-inc/Baichuan-13B-Chat","Baichuan2-7B": "baichuan-inc/Baichuan2-7B-Base","Baichuan2-13B": "baichuan-inc/Baichuan2-13B-Base","Baichuan2-7B-Chat": "baichuan-inc/Baichuan2-7B-Chat","Baichuan2-13B-Chat": "baichuan-inc/Baichuan2-13B-Chat","InternLM-7B": "internlm/internlm-7b","InternLM-7B-Chat": "internlm/internlm-chat-7b","Qwen-7B": "Qwen/Qwen-7B","Qwen-7B-Chat": "Qwen/Qwen-7B-Chat","XVERSE-13B": "xverse/XVERSE-13B","ChatGLM2-6B-Chat": "THUDM/chatglm2-6b","ChatGLM3-6B-Base": "THUDM/chatglm3-6b-base","ChatGLM3-6B-Chat": "THUDM/chatglm3-6b"} |
是可以适配的。但是你要更新DBGPT- HUB到最新的版本。最新版本里面支持qwen2.5 |
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你好，请问哪里可以看到所支持的全部模型以及对应的template，我在最新版（主分支）找了好久看到的支持的模型都是好早之前的，如有回复万分感谢 |
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先把qwen25的模型下载到本地缓存，可以通过swift下载，这样快一点。 |
非常感谢你的回复，我会尝试一下的，图片里模型是qwen2.5，template是llama2，使用的模型和template不对应也是可以的吗？ |
实际测试是可以的。我跑过。 |
再次感谢！ |
CUDA_VISIBLE_DEVICES=0 python /home/ubuntu/TextToSQL/DB-GPT-Hub/src/dbgpt-hub-sql/dbgpt_hub_sql/train/sft_train.py
--model_name_or_path /home/ubuntu/.cache/modelscope/hub/qwen/Qwen2___5-Coder-7B-Instruct
--do_train
--dataset example_text2sql_train
--max_source_length 2048
--max_target_length 512
--template qwen2_5
--finetuning_type lora
--lora_rank 64
--lora_alpha 32
--lora_target q_proj,v_proj
--output_dir /home/ubuntu/TextToSQL/DB-GPT-Hub/src/dbgpt-hub-sql/dbgpt_hub_sql/output/adapter/Qwen25-7B-Instruct-lora
--overwrite_cache
--overwrite_output_dir
--per_device_train_batch_size 1
--gradient_accumulation_steps 16
--lr_scheduler_type cosine_with_restarts
--logging_steps 500
--save_steps 2000
--learning_rate 2e-4
--num_train_epochs 4
--plot_loss
--bf16
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