jupyter lab fixes (#1139) [skip ci]

* add a basic notebook for lab users in the root

* update notebook and fix cors for jupyter

* cell is code

* fix eval batch size check

* remove intro notebook

src/axolotl/cli/train.py

@@ -3,9 +3,11 @@ CLI to run training on a model
 """
 import logging
 from pathlib import Path
+from typing import Tuple
 
 import fire
 import transformers
+from transformers import PreTrainedModel, PreTrainedTokenizer
 
 from axolotl.cli import (
     check_accelerate_default_config,
@@ -24,19 +26,23 @@ LOG = logging.getLogger("axolotl.cli.train")
 def do_cli(config: Path = Path("examples/"), **kwargs):
     # pylint: disable=duplicate-code
     parsed_cfg = load_cfg(config, **kwargs)
-    print_axolotl_text_art()
-    check_accelerate_default_config()
-    check_user_token()
     parser = transformers.HfArgumentParser((TrainerCliArgs))
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
         return_remaining_strings=True
     )
+    return do_train(parsed_cfg, parsed_cli_args)
 
-    if parsed_cfg.rl:
-        dataset_meta = load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+
+def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
+    print_axolotl_text_art()
+    check_accelerate_default_config()
+    check_user_token()
+    if cfg.rl:
+        dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
     else:
-        dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-    train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+    return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
 
 
 if __name__ == "__main__":

src/axolotl/core/trainer_builder.py

@@ -746,9 +746,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs[
             "per_device_train_batch_size"
         ] = self.cfg.micro_batch_size
-        training_arguments_kwargs[
-            "per_device_eval_batch_size"
-        ] = self.cfg.eval_batch_size
+        if self.cfg.eval_batch_size:
+            training_arguments_kwargs[
+                "per_device_eval_batch_size"
+            ] = self.cfg.eval_batch_size
         training_arguments_kwargs[
             "gradient_accumulation_steps"
         ] = self.cfg.gradient_accumulation_steps

src/axolotl/utils/bench.py

@@ -20,7 +20,8 @@ def check_cuda_device(default_value):
             device = kwargs.get("device", args[0] if args else None)
 
             if (
-                not torch.cuda.is_available()
+                device is None
+                or not torch.cuda.is_available()
                 or device == "auto"
                 or torch.device(device).type == "cpu"
             ):

src/axolotl/utils/models.py

@@ -2,7 +2,7 @@
 import logging
 import math
 import os
-from typing import Any, Optional, Tuple, Union  # noqa: F401
+from typing import Any, Dict, Optional, Tuple, Union  # noqa: F401
 
 import addict
 import bitsandbytes as bnb
@@ -348,7 +348,11 @@ def load_model(
         LOG.info("patching _expand_mask")
         hijack_expand_mask()
 
-    model_kwargs = {}
+    model_kwargs: Dict[str, Any] = {}
+
+    if cfg.model_kwargs:
+        for key, val in model_kwargs.items():
+            model_kwargs[key] = val
 
     max_memory = cfg.max_memory
     device_map = cfg.device_map