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import os |
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from threading import Thread |
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from typing import Iterator |
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import gradio as gr |
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import spaces |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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from transformers import BitsAndBytesConfig |
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nf4_config = BitsAndBytesConfig( |
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load_in_8bit=True, |
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bnb_8bit_use_double_quant=True, |
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bnb_8bit_quant_type="nf8", |
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) |
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MAX_MAX_NEW_TOKENS = 2048 |
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DEFAULT_MAX_NEW_TOKENS = 1024 |
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total_count=0 |
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) |
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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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dict_map = { |
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"òa": "oà", |
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"Òa": "Oà", |
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"ÒA": "OÀ", |
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"óa": "oá", |
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"Óa": "Oá", |
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"ÓA": "OÁ", |
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"ỏa": "oả", |
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"Ỏa": "Oả", |
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"ỎA": "OẢ", |
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"õa": "oã", |
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"Õa": "Oã", |
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"ÕA": "OÃ", |
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"ọa": "oạ", |
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"Ọa": "Oạ", |
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"ỌA": "OẠ", |
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"òe": "oè", |
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"Òe": "Oè", |
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"ÒE": "OÈ", |
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"óe": "oé", |
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"Óe": "Oé", |
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"ÓE": "OÉ", |
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"ỏe": "oẻ", |
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"Ỏe": "Oẻ", |
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"ỎE": "OẺ", |
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"õe": "oẽ", |
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"Õe": "Oẽ", |
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"ÕE": "OẼ", |
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"ọe": "oẹ", |
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"Ọe": "Oẹ", |
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"ỌE": "OẸ", |
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"ùy": "uỳ", |
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"Ùy": "Uỳ", |
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"ÙY": "UỲ", |
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"úy": "uý", |
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"Úy": "Uý", |
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"ÚY": "UÝ", |
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"ủy": "uỷ", |
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"Ủy": "Uỷ", |
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"ỦY": "UỶ", |
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"ũy": "uỹ", |
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"Ũy": "Uỹ", |
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"ŨY": "UỸ", |
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"ụy": "uỵ", |
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"Ụy": "Uỵ", |
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"ỤY": "UỴ", |
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} |
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tokenizer_vi2en = AutoTokenizer.from_pretrained("vinai/vinai-translate-vi2en-v2", src_lang="vi_VN") |
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model_vi2en = AutoModelForSeq2SeqLM.from_pretrained("vinai/vinai-translate-vi2en-v2",device_map="auto") |
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def translate_vi2en(vi_text: str) -> str: |
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for i, j in dict_map.items(): |
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vi_text = vi_text.replace(i, j) |
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input_ids = tokenizer_vi2en(vi_text, return_tensors="pt").to("cuda").input_ids |
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output_ids = model_vi2en.generate( |
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input_ids, |
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decoder_start_token_id=tokenizer_vi2en.lang_code_to_id["en_XX"], |
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num_return_sequences=1, |
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num_beams=5, |
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early_stopping=True |
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) |
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en_text = tokenizer_vi2en.batch_decode(output_ids, skip_special_tokens=True) |
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en_text = " ".join(en_text) |
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return en_text |
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DESCRIPTION="""CODE""" |
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model_id = "deepseek-ai/deepseek-coder-7b-instruct-v1.5" |
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model = AutoModelForCausalLM.from_pretrained(model_id,device_map="auto",torch_dtype=torch.bfloat16) |
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tokenizer=AutoTokenizer.from_pretrained(model_id) |
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tokenizer.use_defaul_system_prompt=True |
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os.system("nvidia-smi") |
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@spaces.GPU |
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def gen( |
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message: str, |
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chat_history: list[tuple[str, str]], |
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system_prompt: str, |
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max_new_tokens: int = 1024, |
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temperature: float = 0.6, |
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top_p: float = 0.9, |
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top_k: int = 50, |
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repetition_penalty: float = 1, |
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)->Iterator[str]: |
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global total_count |
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total_count += 1 |
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print(total_count) |
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os.system("nvidia-smi") |
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conversation = [] |
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message = translate_vi2en(message) |
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if system_prompt: |
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conversation.append({"role": "system", "content": system_prompt}) |
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for user, assistant in chat_history: |
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conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) |
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conversation.append({"role": "user", "content": message}) |
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input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt") |
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if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: |
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input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] |
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gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") |
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input_ids = input_ids.to(model.device) |
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streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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{"input_ids": input_ids}, |
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streamer=streamer, |
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max_new_tokens=max_new_tokens, |
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do_sample=False, |
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top_p=top_p, |
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top_k=top_k, |
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num_beams=1, |
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repetition_penalty=repetition_penalty, |
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eos_token_id=32021 |
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) |
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t = Thread(target=model.generate, kwargs=generate_kwargs) |
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t.start() |
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outputs = [] |
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for text in streamer: |
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outputs.append(text) |
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yield "".join(outputs).replace("<|EOT|>","") |
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chat_interface = gr.ChatInterface( |
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fn=gen, |
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additional_inputs=[ |
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gr.Textbox(label="System prompt", lines=6), |
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gr.Slider( |
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label="Max new tokens", |
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minimum=1, |
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maximum=MAX_MAX_NEW_TOKENS, |
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step=1, |
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value=DEFAULT_MAX_NEW_TOKENS, |
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), |
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gr.Slider( |
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label="Top-p (nucleus sampling)", |
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minimum=0.05, |
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maximum=1.0, |
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step=0.05, |
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value=0.9, |
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), |
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gr.Slider( |
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label="Top-k", |
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minimum=1, |
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maximum=1000, |
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step=1, |
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value=50, |
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), |
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gr.Slider( |
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label="Repetition penalty", |
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minimum=1.0, |
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maximum=2.0, |
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step=0.05, |
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value=1, |
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), |
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], |
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stop_btn=gr.Button("Stop"), |
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examples=[ |
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["implement snake game using pygame"], |
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["Can you explain briefly to me what is the Python programming language?"], |
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["write a program to find the factorial of a number"], |
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], |
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) |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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chat_interface.render() |
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if __name__ == "__main__": |
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demo.queue(max_size=100).launch() |
