Update app.py

app.py

@@ -1,36 +1,21 @@
-import sagemaker
-import boto3
-from sagemaker.huggingface import HuggingFace
+from huggingface_hub import hf_hub_download
 
-try:
-	role = sagemaker.get_execution_role()
-except ValueError:
-	iam = boto3.client('iam')
-	role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
-		
-hyperparameters = {
-	'model_name_or_path':'INSAIT-Institute/BgGPT-7B-Instruct-v0.1',
-	'output_dir':'/opt/ml/model'
-	# add your remaining hyperparameters
-	# more info here https://github.com/huggingface/transformers/tree/v4.37.0/examples/pytorch/language-modeling
-}
+HUGGING_FACE_API_KEY = "<hugging-face-api-key-goes-here>"
 
-# git configuration to download our fine-tuning script
-git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.37.0'}
+# Replace this if you want to use a different model
+model_id = "lmsys/fastchat-t5-3b-v1.0"
+filenames = [ 
+    "pytorch_model.bin", "added_tokens.json", "config.json", "generation_config.json",
+    "special_tokens_map.json", "spiece.model", "tokenizer_config.json"
+]
 
-# creates Hugging Face estimator
-huggingface_estimator = HuggingFace(
-	entry_point='run_clm.py',
-	source_dir='./examples/pytorch/language-modeling',
-	instance_type='ml.p3.2xlarge',
-	instance_count=1,
-	role=role,
-	git_config=git_config,
-	transformers_version='4.37.0',
-	pytorch_version='2.1.0',
-	py_version='py310',
-	hyperparameters = hyperparameters
-)
+for filename in filenames:
+    downloaded_model_path = hf_hub_download(
+        repo_id=model_id,
+        filename=filename,
+        token=HUGGING_FACE_API_KEY
+    )
 
-# starting the train job
-huggingface_estimator.fit()
+    print(downloaded_model_path)
+
+print(downloaded_model_path)