Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,083 Bytes
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import spaces
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
model_path = 'sail/Sailor-14B-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, torch_dtype=torch.bfloat16)
# 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 = 'user'
assistant_role = 'assistant'
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. \
As an AI assistant, you need to answer a series of questions next, which may include languages such as English, Chinese, Thai, Vietnamese, Indonesian, Malay, and so on. \
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.
@spaces.GPU()
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=1024,
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'
]
placeholder = """
<div style="opacity: 0.5;">
<img src="https://raw.githubusercontent.com/sail-sg/sailor-llm/main/misc/banner.jpg" style="width:30%;">
<br>Sailor models are designed to understand and generate text across diverse linguistic landscapes of these SEA regions:
<br>🇮🇩Indonesian, 🇹🇭Thai, 🇻🇳Vietnamese, 🇲🇾Malay, and 🇱🇦Lao.
</div>
"""
chatbot = gr.Chatbot(label='Sailor', placeholder=placeholder)
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, chatbot=chatbot, fill_height=True, examples=prompt_examples, css=css)
demo.launch() # Launching the web interface. |