Upload app.py

app.py

@@ -1,25 +1,8 @@
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-model_name ="NousResearch/Llama-2-7b-chat-hf"
-
-bnb_config = BitsAndBytesConfig(
- load_in_4bit=True,
- bnb_4bit_quant_type="nf4",
- bnb_4bit_compute_dtype=torch.bfloat16,
-)
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name, 
-    torch_dtype=torch.bfloat16, 
-    quantization_config=bnb_config)
-model.config.use_cache = False
-
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-tokenizer.pad_token = tokenizer.eos_token
-tokenizer.padding_side = "right"  # Fix weird overflow issue with fp16 training  
+tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
+model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf") 
 
 def predict(message, chatbot, temperature=0.9, max_new_tokens=256, top_p=0.6, repetition_penalty=1.0,):