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from huggingface_hub import InferenceClient
import gradio as gr
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
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=1.0, max_new_tokens=1048, top_p=1.0, 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=1.0,
minimum=0.0,
maximum=1.0,
step=0.01,
interactive=True,
info="Higher values produce more diverse outputs",
),
gr.Slider(
label="Max new tokens",
value=1048,
minimum=0,
maximum=2048,
step=128,
interactive=True,
info="The maximum numbers of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=1.0,
minimum=0.0,
maximum=1.0,
step=0.01,
interactive=True,
info="Higher values sample more low-probability tokens",
),
gr.Slider(
label="Repetition penalty",
value=1.0,
minimum=1.0,
maximum=2.0,
step=0.01,
interactive=True,
info="Penalize repeated tokens",
)
]
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="Mistral 7B v0.3"
).launch(show_api=False)
gr.load("models/ehristoforu/dalle-3-xl-v2").launch()
gr.load("models/microsoft/Phi-3-mini-4k-instruct").launch()
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