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| import gradio as gr | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer | |
| from threading import Thread | |
| import spaces | |
| # 言語リスト | |
| languages = [ | |
| "English", "Chinese (Simplified)", "Chinese (Traditional)", "Spanish", "Arabic", "Hindi", | |
| "Bengali", "Portuguese", "Russian", "Japanese", "German", "French", "Urdu", "Indonesian", | |
| "Italian", "Turkish", "Korean", "Vietnamese", "Tamil", "Marathi", "Telugu", "Persian", | |
| "Polish", "Dutch", "Thai", "Gujarati", "Romanian", "Ukrainian", "Malay", "Kannada", "Oriya (Odia)", | |
| "Burmese (Myanmar)", "Azerbaijani", "Uzbek", "Kurdish (Kurmanji)", "Swedish", "Filipino (Tagalog)", | |
| "Serbian", "Czech", "Hungarian", "Greek", "Belarusian", "Bulgarian", "Hebrew", "Finnish", | |
| "Slovak", "Norwegian", "Danish", "Sinhala", "Croatian", "Lithuanian", "Slovenian", "Latvian", | |
| "Estonian", "Armenian", "Malayalam", "Georgian", "Mongolian", "Afrikaans", "Nepali", "Pashto", | |
| "Punjabi", "Kurdish", "Kyrgyz", "Somali", "Albanian", "Icelandic", "Basque", "Luxembourgish", | |
| "Macedonian", "Maltese", "Hawaiian", "Yoruba", "Maori", "Zulu", "Welsh", "Swahili", "Haitian Creole", | |
| "Lao", "Amharic", "Khmer", "Javanese", "Kazakh", "Malagasy", "Sindhi", "Sundanese", "Tajik", "Xhosa", | |
| "Yiddish", "Bosnian", "Cebuano", "Chichewa", "Corsican", "Esperanto", "Frisian", "Galician", "Hausa", | |
| "Hmong", "Igbo", "Irish", "Kinyarwanda", "Latin", "Samoan", "Scots Gaelic", "Sesotho", "Shona", | |
| "Sotho", "Swedish", "Uyghur" | |
| ] | |
| tokenizer = AutoTokenizer.from_pretrained("aixsatoshi/Honyaku-13b") | |
| model = AutoModelForCausalLM.from_pretrained("aixsatoshi/Honyaku-13b", 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 = [2] | |
| for stop_id in stop_ids: | |
| if input_ids[0][-1] == stop_id: | |
| return True | |
| return False | |
| def predict(message, history, tokens, temperature, language): | |
| tag = "<" + language.lower() + ">" | |
| history_transformer_format = history + [[message, ""]] | |
| stop = StopOnTokens() | |
| messages = "".join(["".join(["\n<english>:"+item[0]+"</english>\n", tag+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=int(tokens), | |
| temperature=float(temperature), | |
| do_sample=True, | |
| top_p=0.95, | |
| top_k=20, | |
| repetition_penalty=1.15, | |
| 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 | |
| # Gradioインタフェースの設定 | |
| demo = gr.ChatInterface( | |
| fn=predict, | |
| title="Honyaku-13b webui", | |
| description="Translate using Honyaku-7b model", | |
| additional_inputs=[ | |
| gr.Slider(100, 4096, value=1000, label="Tokens"), | |
| gr.Slider(0.0, 1.0, value=0.3, label="Temperature"), | |
| gr.Dropdown(choices=languages, value="Japanese", label="Language") | |
| ] | |
| ) | |
| demo.queue().launch() | |