Spaces:
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, 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 = '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=512, | |
| 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. |