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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import os
import urllib.request
import gradio as gr
from llama_cpp import Llama
def download_file(file_link, filename):
# Checks if the file already exists before downloading
if not os.path.isfile(filename):
urllib.request.urlretrieve(file_link, filename)
print("File downloaded successfully.")
else:
print("File already exists.")
# Dowloading GGML model from HuggingFace
ggml_model_path = "https://huggingface.co/CRD716/ggml-vicuna-1.1-quantized/resolve/main/ggml-vicuna-7b-1.1-q4_1.bin"
filename = "ggml-vicuna-7b-1.1-q4_1.bin"
download_file(ggml_model_path, filename)
# n_ctx is max length of prompt
# n_batch is no. of tokens fed to model at a time
llm = Llama(model_path=filename, n_ctx=512, n_batch=126)
def generate_text(prompt="Who is the CEO of Apple?"):
output = llm(
prompt,
max_tokens=256, # max tokens to generate
temperature=0.1,
top_p=0.5,
echo=False, # whether to repeat prompt in output
stop=["#"],
)
output_text = output["choices"][0]["text"].strip()
# Remove Prompt Echo from Generated Text
cleaned_output_text = output_text.replace(prompt, "")
return cleaned_output_text
description = "Vicuna-7B"
examples = [
["What is the capital of France?", "The capital of France is Paris."],
[
"Who wrote the novel 'Pride and Prejudice'?",
"The novel 'Pride and Prejudice' was written by Jane Austen.",
],
["What is the square root of 64?", "The square root of 64 is 8."],
]
gradio_interface = gr.Interface(
fn=generate_text,
inputs=[gr.Textbox(label="Input", lines=2)],
outputs=[gr.Textbox(label="Result", lines=5)],
examples=examples,
title="Vicuna-7B",
description="Getting output from GGML-based model.",
)
gradio_interface.launch()
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