Getting runtime error when using AutoTrain
Hello,I am trying to test out autotrain using allama 2 chatand analpaca datasetbut I have cut it down to 1k rows instead of 52k. I am running on T4 SmallI haven’t changed any configuration options and everything is default.{
  "block_size": 1024,
  "model_max_length": 2048,
  "use_flash_attention_2": false,
  "disable_gradient_checkpointing": false,
  "logging_steps": -1,
  "evaluation_strategy": "epoch",
  "save_total_limit": 1,
  "save_strategy": "epoch",
  "auto_find_batch_size": false,
  "mixed_precision": "fp16",
  "lr": 0.00003,
  "epochs": 3,
  "batch_size": 2,
  "warmup_ratio": 0.1,
  "gradient_accumulation": 1,
  "optimizer": "adamw_torch",
  "scheduler": "linear",
  "weight_decay": 0,
  "max_grad_norm": 1,
  "seed": 42,
  "quantization": "int4",
  "target_modules": "",
  "merge_adapter": false,
  "peft": true,
  "lora_r": 16,
  "lora_alpha": 32,
  "lora_dropout": 0.05
}image1279×790 48.3 KBI get the following runtime error❌ ERROR  | 2023-12-16 17:13:26 | autotrain.trainers.common:wrapper:80 - Expected is_sm80 || is_sm90 to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)I have tried rebuilding with no success
2023-12-22T20:57:54Z
Running into similar problem. Apparently to train LLAMA2 we need GPUs that support sm_80 or sm_90. I am having the same error with an RTX 8000 which apparently is sm_75 capable. Is there a way to know ahead of time which cards are suitable for training a particular LLM?
2023-12-22T22:06:13Z
Ran it on A10G and error went away
2023-12-22T22:09:49Z
I would assume the number of parameters will be a good indicator