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import os | |
from threading import Thread | |
from typing import Iterator | |
import gradio as gr | |
import spaces | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
MAX_MAX_NEW_TOKENS = 2048 | |
DEFAULT_MAX_NEW_TOKENS = 1024 | |
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) | |
DESCRIPTION = """\ | |
# Starling-LM-7B-beta | |
This Space demonstrates [Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) by Nexusflow. | |
""" | |
if not torch.cuda.is_available(): | |
DESCRIPTION += "\n<p>Running on CPU! This demo does not work on CPU.</p>" | |
if torch.cuda.is_available(): | |
model_id = "mahiatlinux/MasherAI-v6-7B" | |
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_4bit=True) | |
tokenizer = AutoTokenizer.from_pretrained(model_id) | |
tokenizer.use_default_system_prompt = False | |
def generate( | |
message: str, | |
chat_history: list[tuple[str, str]], | |
system_prompt: str, | |
max_new_tokens: int = 1024, | |
temperature: float = 0.1, | |
top_p: float = 0.9, | |
top_k: int = 50, | |
repetition_penalty: float = 1.2, | |
) -> Iterator[str]: | |
conversation = [] | |
if system_prompt: | |
conversation.append({"from": "human", "value": "You are an AI assistant."}) | |
for user, assistant in chat_history: | |
conversation.extend([{"from": "human", "value": user}, {"from": "gpt", "value": assistant}]) | |
conversation.append({"from": "human", "value": message}) | |
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt", add_generation_prompt=True) | |
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: | |
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] | |
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") | |
input_ids = input_ids.to(model.device) | |
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) | |
generate_kwargs = dict( | |
{"input_ids": input_ids}, | |
streamer=streamer, | |
max_new_tokens=max_new_tokens, | |
do_sample=True, | |
top_p=top_p, | |
top_k=top_k, | |
temperature=temperature, | |
num_beams=1, | |
repetition_penalty=repetition_penalty, | |
) | |
t = Thread(target=model.generate, kwargs=generate_kwargs) | |
t.start() | |
outputs = [] | |
for text in streamer: | |
outputs.append(text) | |
yield "".join(outputs) | |
chat_interface = gr.ChatInterface( | |
fn=generate, | |
additional_inputs=[ | |
gr.Textbox(label="System prompt", lines=6), | |
gr.Slider( | |
label="Max new tokens", | |
minimum=1, | |
maximum=MAX_MAX_NEW_TOKENS, | |
step=1, | |
value=DEFAULT_MAX_NEW_TOKENS, | |
), | |
gr.Slider( | |
label="Temperature", | |
minimum=0.1, | |
maximum=4.0, | |
step=0.1, | |
value=0.6, | |
), | |
gr.Slider( | |
label="Top-p (nucleus sampling)", | |
minimum=0.05, | |
maximum=1.0, | |
step=0.05, | |
value=0.9, | |
), | |
gr.Slider( | |
label="Top-k", | |
minimum=1, | |
maximum=1000, | |
step=1, | |
value=50, | |
), | |
gr.Slider( | |
label="Repetition penalty", | |
minimum=1.0, | |
maximum=2.0, | |
step=0.05, | |
value=1.2, | |
), | |
], | |
stop_btn=None, | |
examples=[ | |
["Hello there! How are you doing?"], | |
["Can you explain briefly to me what is the Python programming language?"], | |
["Explain the plot of Cinderella in a sentence."], | |
["How many hours does it take a man to eat a Helicopter?"], | |
["Write a 100-word article on 'Benefits of Open-Source in AI research'"], | |
], | |
) | |
with gr.Blocks(css="style.css") as demo: | |
gr.Markdown(DESCRIPTION) | |
chat_interface.render() | |
if __name__ == "__main__": | |
demo.queue(max_size=20).launch() |