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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
import spaces
# Charge le modele
model = AutoModelForCausalLM.from_pretrained(
finetuned_model,
device_map="auto",
trust_remote_code=True,
)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(finetuned_model,
trust_remote_code=True,
padding=True,
truncation=True)
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
@spaces.GPU
def predict(message, history):
history_transformer_format = history + [[message, ""]]
stop = StopOnTokens()
messages = "".join(["".join(["\n[INST]:"+item[0], "\n[/INST]:"+item[1]]) for item in history_transformer_format])
model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=1024,
num_beams=1,
stopping_criteria=StoppingCriteriaList([stop])
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
partial_message = ""
start_flag = True # Flag to ignore initial newline
for new_token in streamer:
if start_flag and new_token == '\n':
continue
start_flag = False
partial_message += new_token
yield partial_message
demo = gr.ChatInterface(predict).launch()
if __name__ == "__main__":
demo.launch() |