| import gradio as gr |
| from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer |
| from threading import Thread |
| import bitsandbytes |
|
|
| tokenizer = AutoTokenizer.from_pretrained("./model/") |
| model = AutoModelForCausalLM.from_pretrained("./model/", device_map="auto", load_in_4bit=True) |
| 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 chat(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(chat).launch() |
| |
