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import os
os.system("pip install ctransformers gradio")
import time
import requests
from tqdm import tqdm
import ctransformers
import gradio as gr
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")
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 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]
def greet(question):
print(question)
output, token_count = complete(f'User: {question}. Can you please answer this as informatively but concisely as possible.\nAssistant: ')
return f"Response: {output} | Tokens: {token_count}"
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch(share=True)
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