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# from huggingface_hub import InferenceClient
# import gradio as gr
# client = InferenceClient(
# "mistralai/Mixtral-8x7B-Instruct-v0.1"
# )
# def format_prompt(message, history):
# prompt = "<s>"
# for user_prompt, bot_response in history:
# prompt += f"[INST] {user_prompt} [/INST]"
# prompt += f" {bot_response}</s> "
# prompt += f"[INST] {message} [/INST]"
# return prompt
# def generate(
# prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
# ):
# temperature = float(temperature)
# if temperature < 1e-2:
# temperature = 1e-2
# top_p = float(top_p)
# generate_kwargs = dict(
# temperature=temperature,
# max_new_tokens=max_new_tokens,
# top_p=top_p,
# repetition_penalty=repetition_penalty,
# do_sample=True,
# seed=42,
# )
# formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
# stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
# output = ""
# for response in stream:
# output += response.token.text
# yield output
# return output
# additional_inputs=[
# gr.Textbox(
# label="System Prompt",
# max_lines=1,
# interactive=True,
# ),
# gr.Slider(
# label="Temperature",
# value=0.9,
# minimum=0.0,
# maximum=1.0,
# step=0.05,
# interactive=True,
# info="Higher values produce more diverse outputs",
# ),
# gr.Slider(
# label="Max new tokens",
# value=256,
# minimum=0,
# maximum=1048,
# step=64,
# interactive=True,
# info="The maximum numbers of new tokens",
# ),
# gr.Slider(
# label="Top-p (nucleus sampling)",
# value=0.90,
# minimum=0.0,
# maximum=1,
# step=0.05,
# interactive=True,
# info="Higher values sample more low-probability tokens",
# ),
# gr.Slider(
# label="Repetition penalty",
# value=1.2,
# minimum=1.0,
# maximum=2.0,
# step=0.05,
# interactive=True,
# info="Penalize repeated tokens",
# )
# ]
# examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
# ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
# ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
# ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
# ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
# ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
# ]
# gr.ChatInterface(
# fn=generate,
# chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
# additional_inputs=additional_inputs,
# title="Mixtral 46.7B",
# examples=examples,
# concurrency_limit=20,
# ).launch(show_api= True)
from huggingface_hub import InferenceClient
import gradio as gr
from PyPDF2 import PdfReader
client = InferenceClient(
"mistralai/Mixtral-8x7B-Instruct-v0.1"
)
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def extract_text_from_pdf(pdf_file):
text = ""
pdf_reader = PdfReader(pdf_file)
for page in pdf_reader.pages:
text += page.extract_text()
return text
def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, pdf_text=None):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
if pdf_text:
formatted_prompt += pdf_text
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield output
return output
additional_inputs=[
gr.Textbox(
label="System Prompt",
max_lines=1,
interactive=True,
),
gr.File("file", label="Upload PDF"),
gr.Slider(
label="Temperature",
value=0.9,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
),
gr.Slider(
label="Max new tokens",
value=256,
minimum=0,
maximum=1048,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.0,
maximum=1,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
),
gr.Slider(
label="Repetition penalty",
value=1.2,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
)
]
examples=[
["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
]
gr.ChatInterface(
fn=generate,
chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
additional_inputs=additional_inputs,
title="Mixtral 46.7B",
examples=examples,
concurrency_limit=20,
).launch(show_api= True)
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