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import os
from threading import Thread
from typing import Iterator
import gradio as gr
import spaces
import torch
import json
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
DESCRIPTION = """\
Shakti is a 250 million parameter language model specifically optimized for resource-constrained environments such as edge devices, including smartphones, wearables, and IoT systems. With support for vernacular languages and domain-specific tasks, Shakti excels in industries such as healthcare, finance, and customer service
For more details, please check [here](https://arxiv.org/pdf/2410.11331v1).
"""
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "2048"))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_id = "SandLogicTechnologies/Shakti-250M"
tokenizer = AutoTokenizer.from_pretrained(model_id, token=os.getenv("SHAKTI"))
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16,
token=os.getenv("SHAKTI")
)
model.eval()
@spaces.GPU(duration=90)
def generate(
message: str,
chat_history: list[tuple[str, str]],
max_new_tokens: int = 1024,
temperature: float = 0.6,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> Iterator[str]:
conversation = []
for user, assistant in chat_history:
conversation.extend(
[
{"role": "user", "content": user},
{"role": "assistant", "content": assistant},
]
)
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(conversation, 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=20.0, 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.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=[
["Can you explain the pathophysiology of hypertension and its impact on the cardiovascular system?"],
["What are the potential side effects of beta-blockers in the treatment of arrhythmias?"],
["What foods are good for boosting the immune system?"],
["What is the difference between a stock and a bond?"],
["How can I start saving for retirement?"],
["What are some low-risk investment options?"],
["What is a power of attorney and when is it used?"],
["What are the key differences between a will and a trust?"],
["How do I legally protect my business name?"]
],
cache_examples=False,
)
with gr.Blocks(css="style.css", fill_height=True) as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
chat_interface.render()
if __name__ == "__main__":
demo.queue(max_size=20).launch() |