Update README.md

README.md

@@ -22,23 +22,33 @@ To start using the Rashik24/Mistral-Instruct-Bangla model, you can use the follo
 
 ```Python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM
+
 
 def load_model(model_name):
-    tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
-    model = AutoModelForCausalLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", trust_remote_code=True)
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "right"
+    config = PeftConfig.from_pretrained("Rashik24/Mistral-Instruct-Bangla")
+    model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2").to("cuda")
+    model = PeftModel.from_pretrained(model, "Rashik24/Mistral-Instruct-Bangla").to("cuda")
+    model.eval()
     return model, tokenizer
 
 def generate_text(prompt, model, tokenizer):
-    inputs = tokenizer.encode(prompt, return_tensors='pt')
-    outputs = model.generate(inputs, max_length=256, num_return_sequences=1)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    with torch.no_grad():
+        generated_code = tokenizer.decode(model.generate(**inputs, max_new_tokens=1024, pad_token_id=2)[0], skip_special_tokens=True)
+    print(generated_code)
+    return generated_code
 
 #Load the model
 model_name = 'Rashik24/Mistral-Instruct-Bangla'
 model, tokenizer = load_model(model_name)
 
-#Example usage
-prompt = "আমি কেমন আছি?"  # Bengali for "How am I?"
+prompt = "একটি গ্রামের বর্ণনা করুন।"
 generated_text = generate_text(prompt, model, tokenizer)
 print(generated_text)
 ```