Spaces:
Sleeping
Sleeping
File size: 2,918 Bytes
c8255b3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
"""
This is a Hugging Face Spaces demo for Fin-RWKV-1B5 attention free finanacial export modal.
Author: Umut (Hope) YILDIRIM <[email protected]>
"""
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
import torch
tokenizer = AutoTokenizer.from_pretrained("umuthopeyildirim/fin-rwkv-1b5")
model = AutoModelForCausalLM.from_pretrained("umuthopeyildirim/fin-rwkv-1b5")
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [29, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
def predict(message, history):
history_transformer_format = history + [[message, ""]]
stop = StopOnTokens()
messages = "".join(["".join(["\nuser :"+item[0], "\nbot:"+item[1]]) # curr_system_message +
for item in history_transformer_format])
print(messages)
model_inputs = tokenizer([messages], return_tensors="pt")
streamer = TextIteratorStreamer(
tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=1024,
do_sample=True,
top_p=0.95,
top_k=1000,
temperature=0.5,
num_beams=1,
stopping_criteria=StoppingCriteriaList([stop])
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
partial_message = ""
for new_token in streamer:
if new_token != '<':
partial_message += new_token
yield partial_message
def generate_text(prompt, tokenizer, model):
# Tokenize the input
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Generate a response
output = model.generate(input_ids, max_length=333, num_return_sequences=1)
# Decode the output
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
return generated_text
title = "# Fin-RWKV: Attention Free Financal Expert (WIP)"
description = """Demo for **Fin-RWKV: Attention Free Financal Expert (WIP)**.
To download the model, please visit [Fin-RWKV: Attention Free Financal Expert (WIP)](https://huggingface.co/umutyildirim/fin-rwkv-1b5)."""
css = """
#img-display-container {
max-height: 100vh;
}
#img-display-input {
max-height: 80vh;
}
#img-display-output {
max-height: 80vh;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(title)
gr.Markdown(description)
with gr.Tab("Chatbot"):
gr.ChatInterface(predict)
with gr.Tab("E-Commerce"):
gr.Markdown("e-commerce")
with gr.Tab("OpenBB"):
gr.Markdown("openbb")
if __name__ == '__main__':
demo.queue().launch()
|