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| from threading import Thread | |
| from typing import Iterator | |
| import gradio as gr | |
| import spaces | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
| MAX_MAX_NEW_TOKENS = 256 | |
| DEFAULT_MAX_NEW_TOKENS = 1024 | |
| MAX_INPUT_TOKEN_LENGTH = 10240 | |
| DESCRIPTION = """\ | |
| # CLEX-7B-Chat-16K | |
| This Space demonstrates model [CLEX-7B-Chat-16K](https://huggingface.co/DAMO-NLP-SG/CLEX-7B-Chat-16K), a Llama-2-7B model fine-tuned using our [CLEX](https://arxiv.org/abs/2310.16450) method. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints). | |
| The web demo supports the maximum input sequence length of 10k now due to the limit of GPU memory, running the demo locally (with larger GPU memory) is highly recommended. | |
| This support of PDF input is tentative. | |
| """ | |
| # LICENSE = """ | |
| # <p/> | |
| # --- | |
| # As a derivate work of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta, | |
| # this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/USE_POLICY.md). | |
| # """ | |
| CITE = """ | |
| If you find our project useful, hope you can star our repo and cite our paper as follows: | |
| ``` | |
| @article{damonlpsg2023clex, | |
| author = {Chen, Guanzheng and Li, Xin and Meng, Zaiqiao and Liang, Shangsong and Bing, Lidong}, | |
| title = {CLEX: Continuous Length Extrapolation for Large Language Models}, | |
| year = 2023, | |
| journal = {arXiv preprint arXiv:2310.16450}, | |
| url = {https://arxiv.org/abs/2310.16450} | |
| } | |
| ``` | |
| """ | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
| # if torch.cuda.is_available(): | |
| model_id = "DAMO-NLP-SG/CLEX-7b-Chat-16K" | |
| from transformers import AutoModelForCausalLM | |
| from modeling_llama import LlamaForCausalLM | |
| # from configuration_clex import CLEXLlamaConfig | |
| # config = CLEXLlamaConfig.from_pretrained( | |
| # model_id | |
| # ) | |
| model = LlamaForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, trust_remote_code=True, low_cpu_mem_usage=True, device_map="auto") | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, use_fast=False) | |
| tokenizer.use_default_system_prompt = False | |
| import PyPDF2 | |
| from io import BytesIO | |
| def process_pdf(input_pdf): | |
| # Read the binary data from the input_pdf | |
| # pdf_data = BytesIO(input_pdf) | |
| # if pdf_data.getvalue().strip() == b'': | |
| # return "" | |
| # Create a PDF reader object | |
| reader = PyPDF2.PdfReader(input_pdf.name) | |
| # Extract the text from each page of the PDF | |
| text = "" | |
| for page in reader.pages: | |
| text += page.extract_text() | |
| # Close the PDF reader and reset the pointer | |
| # reader.close() | |
| # pdf_data.seek(0) | |
| # Return the extracted text | |
| return text | |
| def build_chat(): | |
| from fastchat.model import get_conversation_template | |
| conv = get_conversation_template("vicuna") | |
| conv.append_message(conv.roles[0], prompt) | |
| conv.append_message(conv.roles[1], None) | |
| prompt = conv.get_prompt() | |
| return prompt | |
| import re | |
| def replace_repeated_spaces_and_newlines(text): | |
| # Replace repeated spaces with a single space | |
| text = re.sub(r'\s+', ' ', text) | |
| # Replace repeated newlines with a single newline | |
| text = re.sub(r'\n+', '\n', text) | |
| return text | |
| from fastchat.model import get_conversation_template | |
| def generate( | |
| message: str, | |
| chat_history, | |
| system_prompt: str, | |
| input_pdf: BytesIO = None, | |
| max_new_tokens: int = 1024, | |
| temperature: float = 0.7, | |
| top_p: float = 1.0, | |
| top_k: int = 50, | |
| repetition_penalty: float = 1.0, | |
| ) -> Iterator[str]: | |
| if input_pdf is not None: | |
| pdf_text = process_pdf(input_pdf) | |
| # print(pdf_text) | |
| pdf_text = replace_repeated_spaces_and_newlines(pdf_text) | |
| message += f"\nThis is the beginning of a pdf\n{pdf_text}This is the end of a pdf\n" | |
| conv = get_conversation_template("vicuna") | |
| if system_prompt is not None: | |
| conv.set_system_message(system_prompt) | |
| conv.append_message(conv.roles[0], message) | |
| conv.append_message(conv.roles[1], None) | |
| prompt = conv.get_prompt() | |
| # if system_prompt: | |
| # conversation.append({"role": "system", "content": system_prompt}) | |
| # for user, assistant in chat_history: | |
| # conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) | |
| # conversation.append({"role": "user", "content": message}) | |
| # print(prompt[500:1000]) | |
| # chat = tokenizer.apply_chat_template(conversation, tokenize=False) | |
| inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") | |
| if len(inputs) > MAX_INPUT_TOKEN_LENGTH: | |
| inputs = inputs[-MAX_INPUT_TOKEN_LENGTH:] | |
| gr.Warning("Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") | |
| streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| inputs, | |
| streamer=streamer, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=True, | |
| top_p=top_p, | |
| top_k=top_k, | |
| temperature=temperature, | |
| num_beams=1, | |
| repetition_penalty=repetition_penalty, | |
| ) | |
| t = Thread(target=model.generate, kwargs=generate_kwargs) | |
| t.start() | |
| outputs = [] | |
| for text in streamer: | |
| outputs.append(text) | |
| yield "".join(outputs) | |
| # def generate_with_pdf( | |
| # message: str, | |
| # chat_history, | |
| # system_prompt: str, | |
| # input_pdf: BytesIO = None, | |
| # max_new_tokens: int = 1024, | |
| # temperature: float = 0.6, | |
| # top_p: float = 0.9, | |
| # top_k: int = 50, | |
| # repetition_penalty: float = 1.2, | |
| # ) -> Iterator[str]: | |
| # if input_pdf is not None: | |
| # pdf_text = process_pdf(input_pdf) | |
| # # print(pdf_text) | |
| # message += f"\nThis is the beginning of a pdf\n{pdf_text}This is the end of a pdf\n" | |
| # yield from generate( | |
| # message, | |
| # chat_history, | |
| # system_prompt, | |
| # max_new_tokens, | |
| # temperature, | |
| # top_p, | |
| # top_k, | |
| # repetition_penalty | |
| # ) | |
| chat_interface = gr.ChatInterface( | |
| fn=generate, | |
| additional_inputs=[ | |
| gr.Textbox(label="System prompt", lines=6), | |
| gr.File(label="PDF File", accept=".pdf"), | |
| gr.Slider( | |
| label="Max new tokens", | |
| minimum=1, | |
| maximum=MAX_MAX_NEW_TOKENS, | |
| step=1, | |
| value=DEFAULT_MAX_NEW_TOKENS, | |
| ), | |
| gr.Slider( | |
| label="Temperature", | |
| minimum=0.1, | |
| maximum=4.0, | |
| step=0.1, | |
| value=0.7, | |
| ), | |
| gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| minimum=0.05, | |
| maximum=1.0, | |
| step=0.05, | |
| value=1.0, | |
| ), | |
| gr.Slider( | |
| label="Top-k", | |
| minimum=1, | |
| maximum=1000, | |
| step=1, | |
| value=50, | |
| ), | |
| gr.Slider( | |
| label="Repetition penalty", | |
| minimum=1.0, | |
| maximum=2.0, | |
| step=0.05, | |
| value=1.0, | |
| ), | |
| ], | |
| stop_btn=None, | |
| examples=[ | |
| ["Hello there! How are you doing?"], | |
| ["Can you explain briefly to me what is the Python programming language?"], | |
| ["Explain the plot of Cinderella in a sentence."], | |
| ["How many hours does it take a man to eat a Helicopter?"], | |
| ["Write a 100-word article on 'Benefits of Open-Source in AI research'"], | |
| ], | |
| ) | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") | |
| chat_interface.render() | |
| gr.Markdown(CITE) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch(share=False) |