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| import gradio as gr | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer | |
| from threading import Thread | |
| tokenizer = AutoTokenizer.from_pretrained("physician-ai/mistral-finetuned1") | |
| model = AutoModelForCausalLM.from_pretrained("physician-ai/mistral-finetuned1", torch_dtype=torch.float16) | |
| model = model.to('cuda:0') | |
| class StopOnTokens(StoppingCriteria): | |
| def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: | |
| stop_ids = [29, 0] | |
| for stop_id in stop_ids: | |
| if input_ids[0][-1] == stop_id: | |
| return True | |
| return False | |
| def predict(message, history): | |
| history_transformer_format = history + [[message, ""]] | |
| stop = StopOnTokens() | |
| messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]]) | |
| for item in history_transformer_format]) | |
| model_inputs = tokenizer([messages], 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=1000, | |
| temperature=1.0, | |
| num_beams=1, | |
| stopping_criteria=StoppingCriteriaList([stop]) | |
| ) | |
| t = Thread(target=model.generate, kwargs=generate_kwargs) | |
| t.start() | |
| partial_message = "" | |
| for new_token in streamer: | |
| if new_token != '<': | |
| partial_message += new_token | |
| yield partial_message | |
| gr.ChatInterface(predict).launch() |