[GH-ISSUE #10] finetune 过程出错 #7

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opened 2026-10-26 21:01:34 +00:00 by bsakaguchi · 1 comment

bsakaguchi commented

2026-10-26 21:01:34 +00:00

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Originally created by @ifromeast on GitHub (Jan 16, 2024).
Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-MoE/issues/10

你好，我按照官方指导进行全参数 finetune , 遇到以下问题，请问应该怎么解决呢？

Detected kernel version 4.19.118, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
Traceback (most recent call last):
  File "/data/share_user//training/fintune.py", line 332, in <module>
    train()
  File "/data/share_user/training/fintune.py", line 322, in train
    trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
  File "/root/miniconda3/envs/moe-env/lib/python3.10/site-packages/transformers/trainer.py", line 408, in __init__
    raise ValueError(
ValueError: You cannot perform fine-tuning on purely quantized models. Please attach trainable adapters on top of the quantized model to correctly perform fine-tuning. Please see: https://huggingface.co/docs/transformers/peft for more details

运行的命令是

DATA_PATH="garage-bAInd/Open-Platypus"
OUTPUT_PATH="/test"
MODEL_PATH="deepseek-ai/deepseek-moe-16b-base"

deepspeed training/finetune.py \
    --model_name_or_path $MODEL_PATH \
    --data_path $DATA_PATH \
    --output_dir $OUTPUT_PATH \
    --cache_dir ./cache \
    --num_train_epochs 1 \
    --model_max_length 1024 \
    --per_device_train_batch_size 16 \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps 4 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 100 \
    --save_total_limit 2 \
    --learning_rate 2e-5 \
    --warmup_steps 10 \
    --logging_steps 1 \
    --lr_scheduler_type "cosine" \
    --gradient_checkpointing True \
    --report_to "tensorboard" \
    --deepspeed training/configs/ds_config_zero3.json \
    --bf16 True

Originally created by @ifromeast on GitHub (Jan 16, 2024). Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-MoE/issues/10 你好，我按照官方指导进行全参数 finetune , 遇到以下问题，请问应该怎么解决呢？ ``` Detected kernel version 4.19.118, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. Traceback (most recent call last): File "/data/share_user//training/fintune.py", line 332, in <module> train() File "/data/share_user/training/fintune.py", line 322, in train trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module) File "/root/miniconda3/envs/moe-env/lib/python3.10/site-packages/transformers/trainer.py", line 408, in __init__ raise ValueError( ValueError: You cannot perform fine-tuning on purely quantized models. Please attach trainable adapters on top of the quantized model to correctly perform fine-tuning. Please see: https://huggingface.co/docs/transformers/peft for more details ``` 运行的命令是 ``` DATA_PATH="garage-bAInd/Open-Platypus" OUTPUT_PATH="/test" MODEL_PATH="deepseek-ai/deepseek-moe-16b-base" deepspeed training/finetune.py \ --model_name_or_path $MODEL_PATH \ --data_path $DATA_PATH \ --output_dir $OUTPUT_PATH \ --cache_dir ./cache \ --num_train_epochs 1 \ --model_max_length 1024 \ --per_device_train_batch_size 16 \ --per_device_eval_batch_size 1 \ --gradient_accumulation_steps 4 \ --evaluation_strategy "no" \ --save_strategy "steps" \ --save_steps 100 \ --save_total_limit 2 \ --learning_rate 2e-5 \ --warmup_steps 10 \ --logging_steps 1 \ --lr_scheduler_type "cosine" \ --gradient_checkpointing True \ --report_to "tensorboard" \ --deepspeed training/configs/ds_config_zero3.json \ --bf16 True ```

bsakaguchi closed this issue

2026-10-26 21:01:34 +00:00

bsakaguchi commented

2026-10-26 21:01:35 +00:00

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Owner

@zwd003 commented on GitHub (Jan 16, 2024):

你好，已经修复了，可以pull一下代码

@zwd003 commented on GitHub (Jan 16, 2024): 你好，已经修复了，可以pull一下代码

bsakaguchi referenced this issue

2026-10-26 21:02:00 +00:00

[GH-ISSUE #47] CUDA error: unspecified launch failure #42