Update README.md

README.md

@@ -14,19 +14,13 @@ datasets:
 - mohamedemam/Essay-quetions-auto-grading
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
-We are thrilled to introduce our graduation project, the EM5 model, designed for automated essay grading in both Arabic and English. 📝✨
+We are thrilled to introduce our graduation project, the EM2 model, designed for automated essay grading in both Arabic and English. 📝✨
 
 To develop this model, we first created a custom dataset for training. We adapted the QuAC and OpenOrca datasets to make them suitable for our automated essay grading application.
 
@@ -134,11 +128,12 @@ 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))
 ```