Update README.md

README.md

@@ -68,39 +68,37 @@ The model is not intended for tasks requiring deep reasoning, complex multi-turn
 Here is how you can use this model:
 
 ```python
-from peft import PeftModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-# Define the base model and the adapter model
-base_model = "meta-llama/Meta-Llama-3.1-8B-Instruct"
-adapter_model = "AmirMohseni/Llama-3.1-8B-Instruct-Persian-finetuned-sft"
+# Specify the combined model
+model_name = "AmirMohseni/Llama-3.1-8B-Instruct-Persian-finetuned-sft"
 
-# Load the base model and apply the adapter model using PEFT
-model = AutoModelForCausalLM.from_pretrained(base_model, device_map={"": 0})
-model = PeftModel.from_pretrained(model, adapter_model)
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Ensure pad_token is set (if not already set)
+if tokenizer.pad_token is None:
+    tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})
 
 # Check if CUDA is available, otherwise use CPU
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model = model.to(device)
 
-# Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained(base_model)
-
-# Add a new pad token if necessary
-if tokenizer.pad_token is None:
-    tokenizer.add_special_tokens({'pad_token': '[PAD]'})  # Adding a distinct pad token
-
 # Example usage
 input_text = "چطوری میتونم به اطلاعات درباره ی سهام شرکت های آمریکایی دست پیدا کنم؟"
 
-# Tokenize the input and get both input IDs and attention mask
-inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
-input_ids = inputs['input_ids'].to(device)
-attention_mask = inputs['attention_mask'].to(device)
+# Tokenize the input
+inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).to(device)
 
 # Generate text
-outputs = model.generate(input_ids, attention_mask=attention_mask, max_length=512, pad_token_id=tokenizer.pad_token_id)
+outputs = model.generate(
+    inputs['input_ids'],
+    attention_mask=inputs['attention_mask'],
+    max_length=512,
+    pad_token_id=tokenizer.pad_token_id
+)
 
 # Decode and print the output
 response = tokenizer.decode(outputs[0], skip_special_tokens=True)