Getting runtime error when using AutoTrain

🤗AutoTrain
1

Hello,

I am trying to test out autotrain using a llama 2 chat and an alpaca dataset but I have cut it down to 1k rows instead of 52k. I am running on T4 Small

I 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
}

image
image1279Ă—790 48.3 KB

I 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

2

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?

1 Like
3

Ran it on A10G and error went away

4

I would assume the number of parameters will be a good indicator