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from huggingface_hub import InferenceClient
import gradio as gr
client = InferenceClient(
"mistralai/Mixtral-8x7B-Instruct-v0.1"
)
# Formats the prompt to hold all of the past messages
def format_prompt(message, history):
prompt = "<s>"
# String to add before every prompt
prompt_prefix = "Please correct the grammar in the following sentence: "
prompt_template = "[INST] " + prompt_prefix + "{} [/INST]"
# Iterates through every past user input and response to be added to the prompt
for user_prompt, bot_response in history:
corrected_prompt = prompt_prefix + user_prompt
#prompt += f"[INST] {corrected_prompt} [/INST]"
prompt += prompt_template.format(user_prompt)
prompt += f" {bot_response}</s> "
#print(f"HISTORIC PROMPT: \n\t[INST] {corrected_prompt} [/INST] {bot_response}</s> ")
# Also prepend the prefix to the current message
#corrected_message = prompt_prefix + message
#prompt += f"[INST] {corrected_message} [/INST]"
prompt += prompt_template.format(message)
print("\nPROMPT: \n\t" + prompt)
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=[['Give me the grammatically correct version of the sentence: "We shood buy an car"', None, None, None, None, None, ],
["Give me an example exam question testing students on square roots on basic integers", None, None, None, None, None,],
["Would this block of HTML code run?\n```\n\n```", None, None, None, None, None,],
["I have been to New York last summer.", None, None, None, None, None,],
["We shood buy an car.", 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=False) |