Update README.md

README.md

@@ -35,9 +35,7 @@ You didn't have any oranges on Saturday because you bought 3 oranges yesterday.
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-model_path = "/home/migel/Tess-v2.5-Gemma-2-27b/merged"
-# model_path = "/home/migel/gemma-2-27b"
-# model_path = "google/gemma-2-27b-it"
+model_path = "migtissera/Tess-v2.5-Gemma-2-27B-alpha"
 
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = AutoModelForCausalLM.from_pretrained(
@@ -80,19 +78,12 @@ def generate_text(llm_prompt):
     string = tokenizer.decode(output, skip_special_tokens=True)
     return f"{string}"
 
-
-# conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation. No follow-up questions necessary.<|im_end|>\n<|im_start|>user\n"""
-
 conversation = f"""<bos><start_of_turn>user\n"""
-# conversation = f"""Write a long poem about statins."""
 
 while True:
     user_input = input("You: ")
     llm_prompt = f"{conversation}{user_input}<end_of_turn>\n<start_of_turn>model\n"
-    # llm_prompt = f"{conversation}{user_input}"
     answer = generate_text(llm_prompt)
     print(answer)
     conversation = f"{llm_prompt}{answer}\n<start_of_turn>user\n"
-    json_data = {"prompt": user_input, "answer": answer}
-
 ```