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import os
import logging
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
log_level = os.environ.get("LOG_LEVEL", "WARNING")
logging.basicConfig(encoding='utf-8', level=log_level)
logging.info("Creating Inference Client")
client = InferenceClient(
"mistralai/Mixtral-8x7B-Instruct-v0.1"
)
def format_prompt(message, history):
"""Formats the prompt for the AI"""
logging.info("Formatting Prompt")
logging.debug("Input Message: %s", message)
logging.debug("Input History: %s", history)
prompt = "<|im_start|>system\n" +\
"You are Dolphin, a helpful AI assistant.<|im_end|>"
prompt += "<|im_start|>user\n" + f"{message}<|im_end|>"
prompt += "<|im_start|>assistant"
return prompt
def generate(
prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
logging.info("Generating Response")
logging.debug("Input Prompt: %s", prompt)
logging.debug("Input History: %s", history)
logging.debug("Input System Prompt: %s", system_prompt)
logging.debug("Input Temperature: %s", temperature)
logging.debug("Input Max New Tokens: %s", max_new_tokens)
logging.debug("Input Top P: %s", top_p)
logging.debug("Input Repetition Penalty: %s", repetition_penalty)
logging.info("Converting Parameters to Correct Type")
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
logging.debug("Temperature: %s", temperature)
logging.debug("Top P: %s", top_p)
logging.info("Creating Generate kwargs")
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
logging.debug("Generate Args: %s", generate_kwargs)
formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
logging.debug("Prompt: %s", formatted_prompt)
logging.info("Generating Text")
stream = client.text_generation(
formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
logging.info("Creating Output")
output = ""
for response in stream:
output += response.token.text
yield output
logging.debug("Output: %s", 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 = []
logging.info("Creating Chat Interface")
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 Instruct",
examples=examples,
concurrency_limit=20,
).launch(show_api=False) |