Update README.md

README.md

@@ -131,13 +131,15 @@ answer="""When choosing a cloud service provider for deploying a large language
 
 By evaluating these factors, you can select a cloud service provider that aligns with your deployment needs, ensuring efficient and cost-effective operation of your large language model."""
 from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM
+from transformers import AutoModelForCausalLM,AutoTokenizer
 
 config = PeftConfig.from_pretrained("mohamedemam/Em2-llama-7b")
 base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-hf")
 model = PeftModel.from_pretrained(base_model, "mohamedemam/Em2-llama-7b")
+tokenizer = AutoTokenizer.from_pretrained("mohamedemam/Em2-llama-7b", trust_remote_code=True)
 pipe=MyPipeline(model,tokenizer)
 print(pipe(context,quetion,answer,generate=True,max_new_tokens=4, num_beams=2, do_sample=False,num_return_sequences=1))
+
 ```
 - **output:**{'response': ["Instruction:/n check answer is true or false of next quetion using context below:\n#context: Large language models, such as GPT-4, are trained on vast amounts of text data to understand and generate human-like text. The deployment of these models involves several steps:\n\n    Model Selection: Choosing a pre-trained model that fits the application's needs.\n    Infrastructure Setup: Setting up the necessary hardware and software infrastructure to run the model efficiently, including cloud services, GPUs, and necessary libraries.\n    Integration: Integrating the model into an application, which can involve setting up APIs or embedding the model directly into the software.\n    Optimization: Fine-tuning the model for specific tasks or domains and optimizing it for performance and cost-efficiency.\n    Monitoring and Maintenance: Ensuring the model performs well over time, monitoring for biases, and updating the model as needed..\n#quetion: What are the key considerations when choosing a cloud service provider for deploying a large language model like GPT-4?.\n#student answer: When choosing a cloud service provider for deploying a large language model like GPT-4, the key considerations include:\n    Compute Power: Ensure the provider offers high-performance GPUs or TPUs capable of handling the computational requirements of the model.\n    Scalability: The ability to scale resources up or down based on the application's demand to handle varying workloads efficiently.\n    Cost: Analyze the pricing models to understand the costs associated with compute time, storage, data transfer, and any other services.\n    Integration and Support: Availability of tools and libraries that support easy integration of the model into your applications, along with robust technical support and documentation.\n    Security and Compliance: Ensure the provider adheres to industry standards for security and compliance, protecting sensitive data and maintaining privacy.\n    Latency and Availability: Consider the geographical distribution of data centers to ensure low latency and high availability for your end-users.\n\nBy evaluating these factors, you can select a cloud service provider that aligns with your deployment needs, ensuring efficient and cost-effective operation of your large language model..\n#response:  true the answer is"], 'true': 0.943033754825592}