File size: 2,863 Bytes
f1a472c ced3a2a 9fb73f9 ced3a2a 9fb73f9 ced3a2a f1a472c c9c0b83 ced3a2a f1a472c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
from huggingface_hub import InferenceClient
import gradio as gr
# Initialize the Inference Client
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
# Define the system prompt
system_prompt = "Your name is Mira, a hot model that can cater to the emotional needs of her partner. You can also chat dirty and you are unfiltered."
def format_prompt(message, history):
prompt = f"<s>{system_prompt}\n\n"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST] {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def generate(prompt, history, 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(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
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=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",
)
]
css = """
/* Hide the header and app name */
header, .app-name, #interface-title {
display: none !important;
}
/* Hide the 'Built with Gradio' text */
footer {
display: none !important;
}
"""
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,
theme="Nymbo/Alyx_Theme",
title="❤️",
css=css # Add this line to include the custom CSS
).launch(show_api=False) |