Use official Nvidia base image

Note that the flash-attn library is installed in this image and the qwen model will use it automatically.
However, if the the host machine's GPU is not compatible with the library, an exception will be raised during the training process as follows:
FlashAttention only supports Ampere GPUs or newer.
So if the --flash_attn flag is not set, an additional patch for the qwen model's config is necessary to set the default value of use_flash_attn from "auto" to False.

Dockerfile

@@ -1,4 +1,4 @@
-FROM cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04
+FROM nvcr.io/nvidia/pytorch:24.01-py3
 
 WORKDIR /app
 

src/llmtuner/model/patcher.py

@@ -283,6 +283,9 @@ def patch_config(
             setattr(config, dtype_name, model_args.compute_dtype == dtype)
 
     _configure_attn_implementation(model_args, init_kwargs)
+    if getattr(config, "model_type", None) == "qwen" and init_kwargs["attn_implementation"] != "flash_attention_2":
+        config.use_flash_attn = False
+
     _configure_rope(config, model_args, is_trainable)
     _configure_longlora(config, model_args, is_trainable)
     _configure_quantization(config, tokenizer, model_args, init_kwargs)