Update README.md

README.md

@@ -66,28 +66,27 @@ from peft import PeftModel, PeftConfig
 from transformers import AutoModelForCausalLM , BitsAndBytesConfig
 import torch
 
-#config = PeftConfig.from_pretrained("ayoubkirouane/Llama2_13B_startup_hf")
 bnb_config = BitsAndBytesConfig(
     load_in_4bit=True,
     bnb_4bit_quant_type="nf4",
     bnb_4bit_compute_dtype=getattr(torch, "float16"),
     bnb_4bit_use_double_quant=False)
 model = AutoModelForCausalLM.from_pretrained(
-        "meta-llama/Llama-2-7b-hf",
+        "meta-llama/Llama-2-13b-chat-hf",
         quantization_config=bnb_config,
         device_map={"": 0})
 model.config.use_cache = False
 model.config.pretraining_tp = 1
-model = PeftModel.from_pretrained(model, "TuningAI/Llama2_7B_Cover_letter_generator")
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf" , trust_remote_code=True)
+model = PeftModel.from_pretrained(model, "TuningAI/Llama2_13B_startup_Assistant")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf" , trust_remote_code=True)
 tokenizer.pad_token = tokenizer.eos_token
 tokenizer.padding_side = "right"
-Instruction = "Given a user's information about the target job, you will generate a Cover letter for this job based on this information."
+system_message = "Given a user's startup-related question in English, you will generate a thoughtful answer in English."
 while 1:
   input_text = input(">>>")
   logging.set_verbosity(logging.CRITICAL)
-  prompt = f"### Instruction\n{Instruction}.\n ###Input \n\n{input_text}. ### Output:"
-  pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer,max_length=400)
+  prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n {input_text}. [/INST]"
+  pipe = pipeline(task="text-generation", model=new_model, tokenizer=tokenizer, max_length=512)
   result = pipe(prompt)
   print(result[0]['generated_text'].replace(prompt, ''))
 ```