File size: 4,675 Bytes
b262a14 b21b992 b262a14 34d79f8 b21b992 ec07b24 b262a14 ec07b24 b262a14 0298010 b262a14 0122097 34d79f8 bf14f3d 34d79f8 25a236c 34d79f8 b262a14 0298010 b262a14 |
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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
import spaces
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
import traceback
model_path = 'infly/OpenCoder-8B-Instruct'
# 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 = [96539] # 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. \
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, ""]]
try:
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_messages = []
print(f'history: {history}')
for i, item in enumerate(history):
model_messages.append({"role": user_role, "content": item[0]})
model_messages.append({"role": assistant_role, "content": item[1]})
model_messages.append({"role": user_role, "content": message})
print(f'model_messages: {model_messages}')
print(f'model_final_inputs: {tokenizer.apply_chat_template(model_messages, add_generation_prompt=True, tokenize=False)}', flush=True)
model_inputs = tokenizer.apply_chat_template(model_messages, add_generation_prompt=True, return_tensors="pt").to(device)
# 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=False,
# stopping_criteria=StoppingCriteriaList([stop])
)
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
except Exception as e:
print(traceback.format_exc())
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='OpenCoder', placeholder=None)
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. |