Update README.md

README.md

@@ -14,11 +14,26 @@ This model is a instruction fine-tuned version of FLAN-T5 Large for text transfo
 ## Usage
 
 ```python
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 # Load model and tokenizer
-model = AutoModelForSeq2SeqLM.from_pretrained("sugiv/bluey-poor-flant5")
-tokenizer = AutoTokenizer.from_pretrained("sugiv/bluey-poor-flant5")
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from peft import PeftModel, PeftConfig
+
+# Load the PEFT configuration
+peft_model_id = "sugiv/bluey-poor-flant5"
+peft_config = PeftConfig.from_pretrained(peft_model_id)
+
+# Load the base model
+base_model = AutoModelForSeq2SeqLM.from_pretrained(peft_config.base_model_name_or_path)
+
+# Load the PEFT model
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)
+
+# Set the model to evaluation mode
+model.eval()
 
 def generate_transform_prompt(input_text, filter_combination):
     return f'''You are an advanced text transformation AI. Your task is to {filter_combination['Task']} the given input text according to the specified parameters. {filter_combination['Task'].capitalize()}ing means expressing the same meaning using different words, while maintaining the original intent. Always correct spelling and grammatical errors implicitly.
@@ -60,6 +75,4 @@ prompt = generate_transform_prompt(input_text, filter_combination)
 inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
 outputs = model.generate(**inputs, max_length=150, num_return_sequences=1)
 transformed_text = tokenizer.decode(outputs, skip_special_tokens=True)
-print(transformed_text)
-
-This model is fine-tuned on a synthetic dataset for text transformation tasks. It can perform various transformations based on specified parameters such as tone, target audience, complexity, purpose, style, and verbosity.
+print(transformed_text)