Update app.py

app.py

@@ -1,4 +1,4 @@
-'''from huggingface_hub import InferenceClient
+from huggingface_hub import InferenceClient
 import gradio as gr
 import os
 import random
@@ -175,45 +175,4 @@ gr.ChatInterface(
  title="LLaVa 56B Large Language Virtual Assiatant",
  examples=examples,
  concurrency_limit=20,
-).launch(share=True,show_api=True)'''
-
-
-
-
-
-import sagemaker
-import boto3
-from sagemaker.huggingface import HuggingFace
-
-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':'mistralai/Mixtral-8x7B-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
-}
-
-# git configuration to download our fine-tuning script
-git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.37.0'}
-
-# 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='4.37.0',
- pytorch_version='2.1.0',
- py_version='py310',
- hyperparameters = hyperparameters
-)
-
-# starting the train job
-huggingface_estimator.fit()
+).launch(share=True,show_api=True)