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Running
on
Zero
| import gradio as gr | |
| import torch | |
| import spaces | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer | |
| from threading import Thread | |
| # Loading the tokenizer and model from Hugging Face's model hub. | |
| if torch.cuda.is_available(): | |
| tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-6B-Chat") | |
| model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-6B-Chat", torch_dtype=torch.float16, device_map="auto") | |
| # 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 = [2] # 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 | |
| # Function to generate model predictions. | |
| def predict(message, history): | |
| stop = StopOnTokens() | |
| conversation = [] | |
| for user, assistant in history: | |
| conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) | |
| conversation.append({"role": "user", "content": message}) | |
| prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True) | |
| model_inputs = tokenizer(prompt, 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=50, | |
| temperature=0.7, | |
| repetition_penalty=1.0, | |
| num_beams=1, | |
| 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 '</s>' in partial_message: # Breaking the loop if the stop token is generated. | |
| break | |
| yield partial_message | |
| # Setting up the Gradio chat interface. | |
| gr.ChatInterface(predict, | |
| title="Yi-6B Chat Demo", | |
| description="Warning. All answers are generated and may contain inaccurate information.", | |
| examples=['How do you cook fish?', 'Who is the president of the United States?'] | |
| ).launch() # Launching the web interface. | |