Update config.json

config.json

@@ -1,33 +1,38 @@
-import sagemaker
-import boto3
-from sagemaker.huggingface import HuggingFace
-
-# gets role for executing training job
-iam_client = boto3.client('iam')
-role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
-hyperparameters = {
-	'model_name_or_path':'mICHPl/MINI_AI',
-	'output_dir':'/opt/ml/model'
-	# add your remaining hyperparameters
-	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling
-}
-
-# git configuration to download our fine-tuning script
-git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}
-
-# 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='4.17.0',
-	pytorch_version='1.10.2',
-	py_version='py38',
-	hyperparameters = hyperparameters
-)
-
-# starting the train job
-huggingface_estimator.fit()
+{
+  "_name_or_path": "./output-MINI_AI",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "conversational": {
+      "max_length": 1000
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.20.1",
+  "use_cache": true,
+  "vocab_size": 50257
+}