Create app.py

app.py

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+import os
+os.system('pip install ctransformers')
+
+import ctransformers
+import time
+import requests
+from tqdm import tqdm
+
+
+import uuid
+#Get the model file - you will need Expandable Storage to make this work
+
+if not os.path.isfile('llama-2-7b.ggmlv3.q4_K_S.bin'):
+  print("Downloading Model from HuggingFace")
+  url = "https://huggingface.co/TheBloke/Llama-2-7B-GGML/resolve/main/llama-2-7b.ggmlv3.q4_K_S.bin"
+  response = requests.get(url, stream=True)
+  total_size_in_bytes= int(response.headers.get('content-length', 0))
+  block_size = 1024 #1 Kibibyte
+  progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
+  with open('llama-2-7b.ggmlv3.q4_K_S.bin', 'wb') as file:
+      for data in response.iter_content(block_size):
+          progress_bar.update(len(data))
+          file.write(data)
+  progress_bar.close()
+  if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
+      print("ERROR, something went wrong")
+  
+#Sets up the transformer library and adds in the Llama-2 model
+
+configObj = ctransformers.Config(stop=["\n", 'User'])
+config = ctransformers.AutoConfig(config=configObj, model_type='llama')
+config.config.stop = ["\n"]
+
+llm = ctransformers.AutoModelForCausalLM.from_pretrained('./llama-2-7b.ggmlv3.q4_K_S.bin', config=config)
+print("Loaded model")
+
+def time_it(func):
+    def wrapper(*args, **kwargs):
+        start_time = time.time()
+        result = func(*args, **kwargs)
+        end_time = time.time()
+        execution_time = end_time - start_time
+        print(f"Function '{func.__name__}' took {execution_time:.6f} seconds to execute.")
+        return result
+    return wrapper
+
+def complete(prompt, stop=["User", "Assistant"]):
+  tokens = llm.tokenize(prompt)
+  token_count = 0
+  output = ''
+  for token in llm.generate(tokens):
+    token_count += 1
+    result = llm.detokenize(token)
+    output += result
+    for word in stop:
+      if word in output:
+        print('\n')
+        return [output, token_count]
+    print(result, end='',flush=True)
+
+  print('\n')
+  return [output, token_count]
+
+while True:
+  question = input("\nWhat is your question? > ")
+  start_time = time.time()
+  output, token_count = complete(f'User: {question}. Can you please answer this as informative but concisely as possible.\nAssistant: ')
+  end_time = time.time()
+  execution_time = end_time - start_time
+  print(f"{token_count} tokens generated in {execution_time:.6f} seconds.\n{token_count/execution_time} tokens per second")
+