change model

app.py

@@ -1,22 +1,23 @@
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, GPT2Model, GPT2Tokenizer
 from transformers.utils import logging
 import gradio as gr
+#import spaces
 
 # Define the logger instance for the transformers library
 logger = logging.get_logger("transformers")
 
 # Load the model and tokenizer
-model_name = "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ" #"openai-community/gpt2" or "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ" or "TheBloke/Llama-2-7B-Chat-GGML" or "TheBloke/zephyr-7B-beta-GPTQ"
+model_name = "openai-community/gpt2" #"openai-community/gpt2" or "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ" or "TheBloke/Llama-2-7B-Chat-GGML" or "TheBloke/zephyr-7B-beta-GPTQ"
 tokenizer = AutoTokenizer.from_pretrained(model_name,use_fast=True)
-#model = AutoModelForSeq2SeqLM.from_pretrained(model_name) 
 model = AutoModelForCausalLM.from_pretrained(model_name,device_map="auto",trust_remote_code=False,revision="main")
 
 #transfer model on GPU
-model.to("cuda")
+#model.to("cuda")
 
 # Generate text using the model and tokenizer
+#@spaces.GPU(duration=60)
 def generate_text(input_text):
-    input_ids = tokenizer.encode(input_text, return_tensors="pt").to("cuda")
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")#.to("cuda")
     #attention_mask = input_ids.ne(tokenizer.pad_token_id).long()
     output = model.generate(input_ids, max_new_tokens=512, top_k=50, top_p=0.95, temperature=0.7, do_sample=True)# attention_mask=attention_mask, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7, do_sample=True)
     return tokenizer.decode(output[0])