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Running
on
Zero
Running
on
Zero
import spaces | |
import gradio as gr | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer | |
from threading import Thread | |
model_path = 'sail/Sailor-7B-Chat' | |
# Loading the tokenizer and model from Hugging Face's model hub. | |
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) | |
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True) | |
# using CUDA for an optimal experience | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
model = model.to(device) | |
# Defining a custom stopping criteria class for the model's text generation. | |
class StopOnTokens(StoppingCriteria): | |
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: | |
stop_ids = [151645] # IDs of tokens where the generation should stop. | |
for stop_id in stop_ids: | |
if input_ids[0][-1] == stop_id: # Checking if the last generated token is a stop token. | |
return True | |
return False | |
system_role= 'system' | |
user_role = 'question' | |
assistant_role = "answer" | |
sft_start_token = "<|im_start|>" | |
sft_end_token = "<|im_end|>" | |
ct_end_token = "<|endoftext|>" | |
system_prompt= \ | |
'You are an AI assistant named Sailor created by Sea AI Lab. \ | |
Your answer should be friendly, unbiased, faithful, informative and detailed.' | |
system_prompt = f"<|im_start|>{system_role}\n{system_prompt}<|im_end|>" | |
# Function to generate model predictions. | |
def predict(message, history): | |
# history = [] | |
history_transformer_format = history + [[message, ""]] | |
stop = StopOnTokens() | |
# Formatting the input for the model. | |
messages = system_prompt + sft_end_token.join([sft_end_token.join([f"\n{sft_start_token}{user_role}\n" + item[0], f"\n{sft_start_token}{assistant_role}\n" + item[1]]) | |
for item in history_transformer_format]) | |
model_inputs = tokenizer([messages], return_tensors="pt").to(device) | |
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True) | |
generate_kwargs = dict( | |
model_inputs, | |
streamer=streamer, | |
max_new_tokens=256, | |
do_sample=True, | |
top_p= 0.75, | |
top_k= 60, | |
temperature=0.2, | |
num_beams=1, | |
stopping_criteria=StoppingCriteriaList([stop]), | |
repetition_penalty=1.1, | |
) | |
t = Thread(target=model.generate, kwargs=generate_kwargs) | |
t.start() # Starting the generation in a separate thread. | |
partial_message = "" | |
for new_token in streamer: | |
partial_message += new_token | |
if sft_end_token in partial_message: # Breaking the loop if the stop token is generated. | |
break | |
yield partial_message | |
css = """ | |
full-height { | |
height: 100%; | |
} | |
""" | |
prompt_examples = [ | |
'How to cook a fish?', | |
'Cara memanggang ikan', | |
'วิธีย่างปลา', | |
'Cách nướng cá' | |
] | |
with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as demo: | |
gr.Markdown("""<center><font size=8>Sailor-Chat Bot⚓</center>""") | |
gr.Markdown("""<p align="center"><img src="https://github.com/sail-sg/sailor-llm/raw/main/misc/wide_sailor_banner.jpg" style="height: 110px"/><p>""") | |
gr.ChatInterface(predict, fill_height=True, examples=prompt_examples, css=css) | |
demo.launch() # Launching the web interface. |