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import gradio as gr | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer | |
from threading import Thread | |
title = "🦅Falcon 🗨️ChatBot" | |
description = "Falcon-RW-1B is a 1B parameters causal decoder-only model built by TII and trained on 350B tokens of RefinedWeb." | |
examples = [["How are you?"]] | |
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b",torch_dtype=torch.float16) | |
model = AutoModelForCausalLM.from_pretrained( | |
"tiiuae/falcon-rw-1b", | |
trust_remote_code=True, | |
torch_dtype=torch.float16 | |
) | |
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(["\n<human>:"+item[0], "\n<bot>:"+item[1]]) #curr_system_message + | |
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, | |
do_sample=True, | |
top_p=0.95, | |
top_k=1000, | |
temperature=1.0, | |
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 | |
gr.ChatInterface(predict, | |
title=title, | |
description=description, | |
examples=examples, | |
cache_examples=True, | |
retry_btn=None, | |
undo_btn="Delete Previous", | |
clear_btn="Clear", | |
chatbot=gr.Chatbot(height=300), | |
textbox=gr.Textbox(placeholder="Chat with me")).queue().launch() |