챗봇 애플리케이션을 위한 GPT-NeoXT-Chat-Base-20B 기반 모델이 이제 Amazon SageMaker | 아마존 웹 서비스

%pip install --upgrade sagemaker –quiet

model_id, model_version = "huggingface-textgeneration2-gpt-neoxt-chat-base-20b-fp16", "*"

endpoint_name = name_from_base(f"jumpstart-example-{model_id}") # Retrieve the inference instance type for the specified model.
instance_type = instance_types.retrieve_default( model_id=model_id, model_version=model_version, scope="inference"
) # Retrieve the inference docker container uri.
image_uri = image_uris.retrieve( region=None, framework=None, image_scope="inference", model_id=model_id, model_version=model_version, instance_type=instance_type,
) # Retrieve the model uri.
model_uri = model_uris.retrieve( model_id=model_id, model_version=model_version, model_scope="inference"
) # Create the SageMaker model instance. The inference script is prepacked with the model artifact.
model = Model( image_uri=image_uri, model_data=model_uri, role=aws_role, predictor_cls=Predictor, name=endpoint_name,
) # Set the serializer/deserializer used to run inference through the sagemaker API.
serializer = JSONSerializer()
deserializer = JSONDeserializer() # Deploy the Model.
predictor = model.deploy( initial_instance_count=1, instance_type=instance_type, predictor_cls=Predictor, endpoint_name=endpoint_name, serializer=serializer, deserializer=deserializer
)

payload = { "text_inputs": "<human>: Tell me the steps to make a pizzan<bot>:", "max_length": 500, "max_time": 50, "top_k": 50, "top_p": 0.95, "do_sample": True, "stopping_criteria": ["<human>"],
}
response = predictor.predict(payload)
print(response[0][0]["generated_text"])

<human>: Tell me the steps to make a pizza
<bot>: 1. Choose your desired crust, such as thin-crust or deep-dish. 2. Preheat the oven to the desired temperature. 3. Spread sauce, such as tomato or garlic, over the crust. 4. Add your desired topping, such as pepperoni, mushrooms, or olives. 5. Add your favorite cheese, such as mozzarella, Parmesan, or Asiago. 6. Bake the pizza according to the recipe instructions. 7. Allow the pizza to cool slightly before slicing and serving.
<human>:

class JumpStartOpenChatKitShell(cmd.Cmd): intro = ( "Welcome to the OpenChatKit chatbot shell, modified to use a SageMaker JumpStart endpoint! Type /help or /? to " "list commands. For example, type /quit to exit shell.n" ) prompt = ">>> " human_id = "<human>" bot_id = "<bot>" def __init__(self, predictor: Predictor, cmd_queue: Optional[List[str]] = None, **kwargs): super().__init__() self.predictor = predictor self.payload_kwargs = kwargs self.payload_kwargs["stopping_criteria"] = [self.human_id] if cmd_queue is not None: self.cmdqueue = cmd_queue def preloop(self): self.conversation = Conversation(self.human_id, self.bot_id) def precmd(self, line): command = line[1:] if line.startswith('/') else 'say ' + line return command def do_say(self, arg): self.conversation.push_human_turn(arg) prompt = self.conversation.get_raw_prompt() payload = {"text_inputs": prompt, **self.payload_kwargs} response = self.predictor.predict(payload) output = response[0][0]["generated_text"][len(prompt):] self.conversation.push_model_response(output) print(self.conversation.get_last_turn()) def do_reset(self, arg): self.conversation = Conversation(self.human_id, self.bot_id) def do_hyperparameters(self, arg): print(f"Hyperparameters: {self.payload_kwargs}n") def do_quit(self, arg): return True

cmd_queue = [ "Hello!",
]
JumpStartOpenChatKitShell( endpoint_name=endpoint_name, cmd_queue=cmd_queue, max_new_tokens=128, do_sample=True, temperature=0.6, top_k=40,
).cmdloop()

Welcome to the OpenChatKit chatbot shell, modified to use a SageMaker JumpStart endpoint! Type /help or /? to list commands. For example, type /quit to exit shell. <<< Hello! How may I help you today? >>> What is the capital of US? <<< The capital of US is Washington, D.C. >>> How far it is from PA ? <<< It is approximately 1100 miles.

Welcome to the OpenChatKit chatbot shell, modified to use a SageMaker JumpStart endpoint! Type /help or /? to list commands. For example, type /quit to exit shell. <<< Hello! How may I help you today? >>> What is the sentiment of this sentence "The weather is good and I am going to play outside, it is sunny and warm" <<< POSITIVE >>> What is the sentiment of this sentence " The news this morning was tragic and it created lot of fear and concerns in city" <<< NEGATIVE

Welcome to the OpenChatKit chatbot shell, modified to use a SageMaker JumpStart endpoint! Type /help or /? to list commands. For example, type /quit to exit shell. <<< Hello! How may I help you today? >>> Write a short summary for this text: Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document. Use Amazon Comprehend to create new products based on understanding the structure of documents. For example, using Amazon Comprehend you can search social networking feeds for mentions of products or scan an entire document repository for key phrases. nYou can access Amazon Comprehend document analysis capabilities using the Amazon Comprehend console or using the Amazon Comprehend APIs. You can run real-time analysis for small workloads or you can start asynchronous analysis jobs for large document sets. You can use the pre-trained models that Amazon Comprehend provides, or you can train your own custom models for classification and entity recognition. nAll of the Amazon Comprehend features accept UTF-8 text documents as the input. In addition, custom classification and custom entity recognition accept image files, PDF files, and Word files as input. nAmazon Comprehend can examine and analyze documents in a variety of languages, depending on the specific feature. For more information, see Languages supported in Amazon Comprehend. Amazon Comprehend's Dominant language capability can examine documents and determine the dominant language for a far wider selection of languages. <<< Amazon Comprehend is a natural language processing (NLP) service that uses deep learning and other advanced AI techniques to extract insights from unstructured text. >>> What kind of files does custom entity recognition accept as inputs? <<< Image files, PDF files, and Word files

Welcome to the OpenChatKit chatbot shell, modified to use a SageMaker JumpStart endpoint! Type /help or /? to list commands. For example, type /quit to exit shell. <<< Hello! How may I help you today? >>> Generate a table summarizing the options outlined in this email. Team, we need to plan a project. The first task is for creating a web app and will take 3 developers, 2 testers with duration of 3 weeks. This is priority 1 The second task is for refactoring a web app and will take 2 developers, 5 testers with duration of 4 weeks. This is priority 2 A markdown table with 2 rows and six columns: (1) Task ID , (2) Task Description, (3) Developers, (4) Testers, (5) Duration, (6) Priority <<< | Task ID | Task Description | Developers | Testers | Duration | Priority |
| --------- | --------- | --------- | --------- | --------- | --------- |
| 1 | Create a web app | 3 | 2 | 3 weeks | 1 |
| 2 | Refactor a web app | 2 | 5 | 4 weeks | 2 |

>>> /hyperparameters <<< Hyperparameters: {'max_new_tokens': 128, 'do_sample': True, 'temperature': 0.6, 'top_k': 40, 'stopping_criteria': ['<human>']} >>> /quit