Update app.py

app.py

@@ -8,13 +8,10 @@ import time
 #import torch
 import pandas as pd
 
-#from transformers import AutoTokenizer, AutoModelForCausalLM
-#from transformers import AutoModelForCausalLM, AutoModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
 from transformers import TextIteratorStreamer
 from threading import Thread
-#from transformers import LlamaForCausalLM, LlamaTokenizer
-#git lfs install
-from ctransformers import AutoModelForCausalLM, AutoConfig, Config, AutoTokenizer
+#from ctransformers import AutoModelForCausalLM, AutoConfig, Config, AutoTokenizer
 
 #from huggingface_hub import InferenceClient
 from huggingface_hub import Repository, upload_file
@@ -29,31 +26,14 @@ historylog = [{
         "Output": ''
 }]
 
-i_temperature = 0.30 
-i_max_new_tokens=1100
-i_repetitionpenalty = 1.2
-i_contextlength=12048
-logfile = 'TinyLlama.1B.txt'
-
-print("loading model...")
-modelfile="TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
-
-
-conf = AutoConfig(Config(temperature=i_temperature, 
-                         repetition_penalty=i_repetitionpenalty, 
-                         batch_size=64,
-                         max_new_tokens=i_max_new_tokens, 
-                         context_length=i_contextlength))
-llm_model = AutoModelForCausalLM.from_pretrained(modelfile,
-                                           model_type="llama",
-                                           config=conf)
+llm_model = "TinyLlama/TinyLlama-1.1B-Chat-v0.6"
 
 
 # TheBloke/Llama-2-7B-Chat-GGML , TinyLlama/TinyLlama-1.1B-Chat-v1.0 , microsoft/Phi-3-mini-4k-instruct, health360/Healix-1.1B-V1-Chat-dDPO
 # TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF and tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf not working
 
 model = AutoModelForCausalLM.from_pretrained(llm_model)
-#tokenizer = AutoTokenizer.from_pretrained(llm_model)
+tokenizer = AutoTokenizer.from_pretrained(llm_model)
 #initiate model and tokenizer
 
 data = load_dataset("Namitg02/Test", split='train', streaming=False)
@@ -83,10 +63,10 @@ print("check2")
 # memory = ConversationBufferMemory(return_messages=True)
 
 
-#terminators = [
-#    tokenizer.eos_token_id, # End-of-Sequence Token that indicates where the model should consider the text sequence to be complete
-#    tokenizer.convert_tokens_to_ids("<|eot_id|>") # Converts a token strings in a single/ sequence of integer id using the vocabulary
-#]
+terminators = [
+    tokenizer.eos_token_id, # End-of-Sequence Token that indicates where the model should consider the text sequence to be complete
+    tokenizer.convert_tokens_to_ids("<|eot_id|>") # Converts a token strings in a single/ sequence of integer id using the vocabulary
+]
 # indicates the end of a sequence