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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")
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