Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,589 Bytes
e55bd08 131a07a e55bd08 131a07a e55bd08 131a07a 15d1015 e55bd08 0474700 e55bd08 |
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 |
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import spaces
from threading import Thread
# Load model and tokenizer
model_name = "Magpie-Align/MagpieLM-4B-Chat-v0.1"
device = "cuda" # the device to load the model onto
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto"
)
model.to(device)
MAX_INPUT_TOKEN_LENGTH = 4096 # You may need to adjust this value
@spaces.GPU(enable_queue=True)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens=2048,
temperature=0.6,
top_p=0.9,
repetition_penalty=1.0,
):
messages = [{"role": "system", "content": system_message}]
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})
input_ids = tokenizer.apply_chat_template(messages, 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=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids=input_ids,
streamer=streamer,
max_new_tokens=max_tokens,
do_sample=True,
top_p=top_p,
temperature=temperature,
repetition_penalty=repetition_penalty,
)
thread = Thread(target=model.generate, kwargs=generate_kwargs)
thread.start()
def stream():
for text in streamer:
yield text
return stream()
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are Magpie, 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=0.6, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.9,
step=0.05,
label="Top-p (nucleus sampling)",
),
gr.Slider(minimum=0.5, maximum=1.5, value=1.0, step=0.1, label="Repetition Penalty"),
],
)
if __name__ == "__main__":
demo.launch(share=True)
|