import torch from PIL import Image import gradio as gr import spaces from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer import os from threading import Thread HF_TOKEN = os.environ.get("HF_TOKEN", None) MODEL_ID = "THUDM/glm-4-9b-chat" MODEL_ID2 = "THUDM/glm-4-9b-chat-1m" MODELS = os.environ.get("MODELS") MODEL_NAME = MODELS.split("/")[-1] TITLE = "

GLM-4-9B

" DESCRIPTION = f'

MODEL: {MODEL_NAME}

' CSS = """ .duplicate-button { margin: auto !important; color: white !important; background: black !important; border-radius: 100vh !important; } """ model = AutoModelForCausalLM.from_pretrained( MODELS, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True, ).to(0).eval() tokenizer = AutoTokenizer.from_pretrained(MODELS,trust_remote_code=True) @spaces.GPU def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int): print(f'message is - {message}') print(f'history is - {history}') 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", return_dict=True).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, do_sample=True, temperature=temperature, repetition_penalty=1.2, eos_token_id=model.config.eos_token_id, ) 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=450) with gr.Blocks(css=CSS) 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=4096, step=1, value=1024, label="Max new tokens", 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()