Spaces:
Running
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Running
on
Zero
File size: 3,433 Bytes
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#!/usr/bin/env python
import os
from collections.abc import Iterator
from threading import Thread
import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
DESCRIPTION = "# FluentlyLM Prinum"
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_id = "fluently-lm/FluentlyLM-Prinum"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16,
)
model.eval()
@spaces.GPU(duration=120)
def generate(
message: str,
chat_history: list[dict],
system_prompt: str = "",
max_new_tokens: int = 1024,
temperature: float = 0.7,
top_p: float = 0.8,
top_k: int = 20,
repetition_penalty: float = 1.05,
) -> Iterator[str]:
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.extend(chat_history.copy())
messages.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
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=30.0, 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)
demo = gr.ChatInterface(
fn=generate,
additional_inputs=[
gr.Textbox(label="System Prompt", value="You are FluentlyLM, created by Project Fluently. You are a helpful assistant."),
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=0.65,
),
gr.Slider(
label="Top-p (nucleus sampling)",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.8,
),
gr.Slider(
label="Top-k",
minimum=1,
maximum=1000,
step=1,
value=20,
),
gr.Slider(
label="Repetition penalty",
minimum=1.0,
maximum=2.0,
step=0.05,
value=1.05,
),
],
stop_btn=None,
examples=[
["Hi! How are you?"],
],
cache_examples=False,
type="messages",
description=DESCRIPTION,
css_paths="style.css",
fill_height=True,
)
if __name__ == "__main__":
demo.launch() |