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import subprocess |
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subprocess.run( |
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'pip install flash-attn --no-build-isolation', |
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env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, |
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shell=True |
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) |
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import os |
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import time |
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import spaces |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import gradio as gr |
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MODEL_LIST = ["internlm/internlm2_5-7b-chat", "internlm/internlm2_5-7b-chat-1m"] |
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HF_TOKEN = os.environ.get("HF_TOKEN", None) |
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MODEL_ID = os.environ.get("MODEL_ID", None) |
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MODEL_NAME = MODEL_ID.split("/")[-1] |
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TITLE = "<h1><center>internlm2.5-7b-chat</center></h1>" |
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|
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DESCRIPTION = f""" |
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<h3>MODEL NOW: <a href="https://hf.co/{MODEL_ID}">{MODEL_NAME}</a></h3> |
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""" |
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PLACEHOLDER = """ |
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<center> |
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<p>InternLM2.5 has open-sourced a 7 billion parameter base model<br> and a chat model tailored for practical scenarios.</p> |
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</center> |
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""" |
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CSS = """ |
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.duplicate-button { |
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margin: auto !important; |
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color: white !important; |
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background: black !important; |
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border-radius: 100vh !important; |
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} |
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h3 { |
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text-align: center; |
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} |
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""" |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_ID, |
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torch_dtype=torch.float16, |
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attn_implementation="flash_attention_2", |
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trust_remote_code=True).cuda() |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True) |
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model = model.eval() |
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@spaces.GPU() |
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def stream_chat( |
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message: str, |
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history: list, |
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temperature: float = 0.8, |
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max_new_tokens: int = 1024, |
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top_p: float = 1.0, |
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top_k: int = 20, |
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penalty: float = 1.2 |
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): |
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print(f'message: {message}') |
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print(f'history: {history}') |
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for resp, history in model.stream_chat( |
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tokenizer, |
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query = message, |
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history = history, |
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max_new_tokens = max_new_tokens, |
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do_sample = False if temperature == 0 else True, |
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top_p = top_p, |
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top_k = top_k, |
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temperature = temperature, |
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): |
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yield resp |
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chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER) |
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with gr.Blocks(css=CSS, theme="soft") as demo: |
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gr.HTML(TITLE) |
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gr.HTML(DESCRIPTION) |
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gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") |
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gr.ChatInterface( |
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fn=stream_chat, |
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chatbot=chatbot, |
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fill_height=True, |
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additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), |
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additional_inputs=[ |
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gr.Slider( |
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minimum=0, |
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maximum=1, |
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step=0.1, |
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value=0.8, |
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label="Temperature", |
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render=False, |
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), |
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gr.Slider( |
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minimum=128, |
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maximum=8192, |
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step=1, |
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value=1024, |
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label="Max New Tokens", |
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render=False, |
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), |
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gr.Slider( |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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value=1.0, |
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label="top_p", |
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render=False, |
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), |
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gr.Slider( |
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minimum=1, |
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maximum=20, |
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step=1, |
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value=20, |
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label="top_k", |
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render=False, |
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), |
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gr.Slider( |
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minimum=0.0, |
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maximum=2.0, |
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step=0.1, |
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value=1.2, |
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label="Repetition penalty", |
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render=False, |
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), |
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], |
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examples=[ |
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["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."], |
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["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."], |
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["Tell me a random fun fact about the Roman Empire."], |
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["Show me a code snippet of a website's sticky header in CSS and JavaScript."], |
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], |
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cache_examples=False, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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