Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,445 Bytes
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import spaces
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
model_name = "Merdeka-LLM/merdeka-llm-3.2b-128k-instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
streamer = TextIteratorStreamer(tokenizer, timeout=300, skip_prompt=True, skip_special_tokens=True)
@spaces.GPU
def respond(
message,
history: list[tuple[str, str]],
# system_message,
max_tokens,
temperature,
top_p,
):
messages = [
{"role": "system", "content": "You are a professional lawyer who is familiar with Malaysia Law."}
]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generate_kwargs = dict(
model_inputs,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
streamer=streamer
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
for new_token in streamer:
if new_token != '<':
response += new_token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
# gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch(
)
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