import os import threading import time import subprocess import torch from transformers import AutoModelForCausalLM, AutoTokenizer import gradio as gr HF_TOKEN = os.environ.get("HF_TOKEN", None) MODEL_ID = os.environ.get("MODEL_ID", None) MODEL_NAME = MODEL_ID.split("/")[-1] TITLE = "

internlm2.5-7b-chat

" DESCRIPTION = f"""

MODEL: {MODEL_NAME}

""" PLACEHOLDER = """

Feel free to test models without any logs.

""" CSS = """ .duplicate-button { margin: auto !important; color: white !important; background: black !important; border-radius: 100vh !important; } h3 { text-align: center; } """ model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.float16, trust_remote_code=True).cuda() tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True) model = model.eval() def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float): conversation = [] for prompt, answer in history: conversation.extend([ {"role": "user", "content": prompt}, {"role": "assistant", "content": answer}, ]) conversation.append({"role": "user", "content": message}) print(f"Conversation is -\n{conversation}") input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) streamer = TextIteratorStreamer(tokenizer, **{"skip_special_tokens": True, "skip_prompt": True, 'clean_up_tokenization_spaces':False,}) generate_kwargs = dict( input_ids=input_ids, streamer=streamer, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, repetition_penalty=penalty, do_sample=True, temperature=temperature, eos_token_id = [2,92542], ) thread = Thread(target=model.generate, kwargs=generate_kwargs) thread.start() buffer = "" for new_text in streamer: buffer += new_text yield buffer chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER) with gr.Blocks(css=CSS, theme="soft") as demo: gr.HTML(TITLE) gr.HTML(DESCRIPTION) gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") gr.ChatInterface( fn=stream_chat, chatbot=chatbot, fill_height=True, additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), additional_inputs=[ gr.Slider( minimum=0, maximum=1, step=0.1, value=0.8, label="Temperature", render=False, ), gr.Slider( minimum=128, maximum=2048, step=1, value=1024, label="Max New Tokens", render=False, ), gr.Slider( minimum=0.0, maximum=1.0, step=0.1, value=0.8, label="top_p", render=False, ), gr.Slider( minimum=1, maximum=20, step=1, value=20, label="top_k", render=False, ), gr.Slider( minimum=0.0, maximum=2.0, step=0.1, value=1.0, label="Repetition penalty", render=False, ), ], examples=[ ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."], ["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."], ["Tell me a random fun fact about the Roman Empire."], ["Show me a code snippet of a website's sticky header in CSS and JavaScript."], ], cache_examples=False, ) if __name__ == "__main__": demo.launch()