import gradio as gr import torch 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. tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") # 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 = [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): history_transformer_format = history + [[message, ""]] stop = StopOnTokens() # Formatting the input for the model. messages = "".join(["".join(["\n<|user|>:" + item[0], "\n<|assistant|>:" + 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=0.95, top_k=50, temperature=0.7, 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 '' 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="Tinyllama_chatBot", description="Ask Tiny llama any questions", examples=['How to cook a fish?', 'Who is the president of US now?'] ).launch() # Launching the web interface.