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from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
from peft import PeftModel, PeftConfig
import torch
import gradio as gr
import random
from textwrap import wrap
EXAMPLES = [
["Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"],
["What's the Everett interpretation of quantum mechanics?"],
["Give me a list of the top 10 dive sites you would recommend around the world."],
["Can you tell me more about deep-water soloing?"],
["Can you write a short tweet about the release of our latest AI model, Falcon LLM?"]
]
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model_id = "tiiuae/falcon-7b-instruct"
model_directory = "Tonic/GaiaMiniMed"
tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True, padding_side="left")
model_config = AutoConfig.from_pretrained(base_model_id)
peft_model = AutoModelForCausalLM.from_pretrained(model_directory, config=model_config)
peft_model = PeftModel.from_pretrained(peft_model, model_directory)
def format_prompt(message, history, system_prompt):
prompt = ""
if system_prompt:
prompt += f"System: {system_prompt}\n"
for user_prompt, bot_response in history:
prompt += f"User: {user_prompt}\n"
prompt += f"Falcon: {bot_response}\n" # Response already contains "Falcon: "
prompt += f"""User: {message}
Falcon:"""
return prompt
seed = 42
def generate(
prompt, history, system_prompt="", temperature=0.9, max_new_tokens=500, top_p=0.95, repetition_penalty=1.0,
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
global seed
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=1.0,
stop_sequences="[END]",
do_sample=True,
seed=seed,
)
seed = seed + 1
formatted_prompt = format_prompt(prompt, history, system_prompt)
try:
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
for stop_str in STOP_SEQUENCES:
if output.endswith(stop_str):
output = output[:-len(stop_str)]
output = output.rstrip()
yield output
yield output
except Exception as e:
raise gr.Error(f"Error while generating: {e}")
return output
additional_inputs=[
gr.Textbox("", label="Optional system prompt"),
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=3000,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.01,
maximum=0.99,
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",
)
]
with gr.Blocks() as demo:
title = "👋🏻Welcome to Tonic's GaiaMiniMed🦅⚕️Falcon Chat🚀"
description = "You can use this Space to test out the current model [(Tonic/GaiaMiniMed)](https://huggingface.co/Tonic/GaiaMiniMed) with chat memory optimized for falcon models or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."
client = gr.Interface(
generate,
examples=EXAMPLES,
additional_inputs=additional_inputs,
theme="ParityError/Anime"
)
# Launch the Gradio interface
client.launch(show_api=True) |