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from huggingface_hub import InferenceClient
import gradio as gr
client = InferenceClient(
"mistralai/Mistral-7B-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, temperature=0.9, max_new_tokens=900, 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(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.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=900,
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",
)
]
css = """
#mkd {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as ai_chat:
gr.HTML("<h1><center>AI Conversation<h1><center>")
gr.HTML("<h3><center>How can I help you? You can converse with me and say more💬<h3><center>")
gr.HTML("<h3><center>To try, select an example below and hit submit<h3><center>")
gr.HTML("<h3><center>Have a wonderful day! 📚<h3><center>")
gr.ChatInterface(
generate,
additional_inputs=additional_inputs,
examples=[["List fun activities in Boston."], ["How to spend a weekend in San Francisco?"], ["What is the secret to life?"], ["Write me a recipe for a quick vegeterain breakfast."],["What is the future for software developers?."],
["Create a plan for daily healthy habbits."], ["What is optogenetic simulation?"], ["How to conduct a neuroscience experiment using holography?"], ["Tell me lifestyle of people living in Auckland, NZ"], ["Make a tour plan for Los Angeles metro area."]]
)
#ai_chat.queue(concurrency_limit=None, max_size=250).launch(debug=True)
ai_chat.queue(max_size=250).launch(debug=True) |