Update README.md

README.md

@@ -54,51 +54,28 @@ It can serve as a base model for further alignment, personalization, or integrat
 ## Get Started with the Model
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "Mehdi-Zogh/MNLP_M3_dpo_model"
-
-# load the tokenizer and the model
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
+prompt = "What are the phases of cell division?"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Mehdi-Zogh/MNLP_M3_dpo_model", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("Mehdi-Zogh/MNLP_M3_dpo_model", device_map="auto", trust_remote_code=True)
+
+# Tokenize
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# Generate response
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=500,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+    eos_token_id=tokenizer.eos_token_id,
 )
 
-# prepare the model input
-prompt = "explain gradient descent in simple terms."
-messages = [
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True,
-    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-# conduct text completion
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=32768
-)
-output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
-
-# parsing thinking content
-try:
-    # rindex finding 151668 (</think>)
-    index = len(output_ids) - output_ids[::-1].index(151668)
-except ValueError:
-    index = 0
-
-thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
-content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
-
-print("thinking content:", thinking_content)
-print("content:", content)
-
+# Decode and print
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
 ```