# import gradio as gr # model_name = "models/THUDM/chatglm2-6b-int4" # gr.load(model_name).lauch() # %%writefile demo-4bit.py from textwrap import dedent # credit to https://github.com/THUDM/ChatGLM2-6B/blob/main/web_demo.py # while mistakes are mine from transformers import AutoModel, AutoTokenizer import gradio as gr import mdtex2html from loguru import logger model_name = "THUDM/chatglm2-6b" model_name = "THUDM/chatglm2-6b-int4" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) # model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda() # 4/8 bit # model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda() import torch has_cuda = torch.cuda.is_available() # has_cuda = False # force cpu if has_cuda: model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda() # 3.92G else: model = AutoModel.from_pretrained(model_name, trust_remote_code=True).half() # .float() .half().float() model = model.eval() _ = """Override Chatbot.postprocess""" def postprocess(self, y): if y is None: return [] for i, (message, response) in enumerate(y): y[i] = ( None if message is None else mdtex2html.convert((message)), None if response is None else mdtex2html.convert(response), ) return y gr.Chatbot.postprocess = postprocess def parse_text(text): """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/""" lines = text.split("\n") lines = [line for line in lines if line != ""] count = 0 for i, line in enumerate(lines): if "```" in line: count += 1 items = line.split('`') if count % 2 == 1: lines[i] = f'
'
            else:
                lines[i] = f'
' else: if i > 0: if count % 2 == 1: line = line.replace("`", "\`") line = line.replace("<", "<") line = line.replace(">", ">") line = line.replace(" ", " ") line = line.replace("*", "*") line = line.replace("_", "_") line = line.replace("-", "-") line = line.replace(".", ".") line = line.replace("!", "!") line = line.replace("(", "(") line = line.replace(")", ")") line = line.replace("$", "$") lines[i] = "
"+line text = "".join(lines) return text def predict(RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values): print(f"Inside predict, user_input1 is - {input}") chatbot.append((parse_text(input), "")) print(f"Inside predict, user_input2 is - {input}") print(f"Inside predict, chatbot is - {chatbot}") print(f"Inside predict, history is - {history}") #if RETRY_FLAG: # history.append() for response, history, past_key_values in model.stream_chat(tokenizer, input, history, past_key_values=past_key_values, return_past_key_values=True, max_length=max_length, top_p=top_p, temperature=temperature): chatbot[-1] = (parse_text(input), parse_text(response)) #print(f"Inside predict, chatbot2 is - {chatbot}") #print(f"Inside predict, history2 is - {history}") yield chatbot, history, past_key_values def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2): if max_length < 100: max_length = 4096 if top_p < 0.1: top_p = 0.8 if temperature <= 0: temperature = 0.01 try: res, _ = model.chat( tokenizer, input, history=[], past_key_values=None, max_length=max_length, top_p=top_p, temperature=temperature, ) # logger.debug(f"{res=} \n{_=}") except Exception as exc: logger.error(f"{exc=}") res = str(exc) return res def reset_user_input(): return gr.update(value='') def reset_state(): return [], [], None # Delete last turn def delete_last_turn(chat, history): if chat and history: chat.pop(-1) history.pop(-1) #history.pop(-1) return chat, history # Regenerate response def retry_last_answer( user_input, chatbot, max_length, top_p, temperature, history, past_key_values ): print(f"chatbot is - {chatbot}") print(f"history is - {history}") print(f"user_input is - {user_input}") print(f"max_length is - {max_length}") print(f"top_p is - {top_p}") print(f"temperature is - {temperature}") print(f"past_key_values type is - {type(past_key_values)}") if chatbot and history: # Removing the previous conversation from chat chatbot.pop(-1) #chatbot[-1] = (chatbot[-1][0],) #history[-1] = (history[-1][0],) # Setting up a flag to capture a retry RETRY_FLAG = True # Getting last message from user user_input = history[-1][0] # Removing bot response from the history history.pop(-1) print(f"popped chatbot is - {chatbot}") print(f"popped history is - {history}") #print(f"user_input is - {user_input}") yield from predict( RETRY_FLAG, user_input, chatbot, max_length, top_p, temperature, history, past_key_values ) with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.HTML("""

ChatGLM2-6B-int4

""") gr.HTML("""
Duplicate SpaceTo avoid the queue and for faster inference Duplicate this Space and upgrade to GPU
""") with gr.Accordion("Info", open=False): _ = """ A query takes from 30 seconds to a few tens of seconds, dependent on the number of words/characters the question and answer contain. * Low temperature: responses will be more deterministic and focused; High temperature: responses more creative. * Suggested temperatures -- translation: up to 0.3; chatting: > 0.4 * Top P controls dynamic vocabulary selection based on context. For a table of example values for different scenarios, refer to [this](https://community.openai.com/t/cheat-sheet-mastering-temperature-and-top-p-in-chatgpt-api-a-few-tips-and-tricks-on-controlling-the-creativity-deterministic-output-of-prompt-responses/172683) If the instance is not on a GPU (T4), it will be very slow. You can try to run the colab notebook [chatglm2-6b-4bit colab notebook](https://colab.research.google.com/drive/1WkF7kOjVCcBBatDHjaGkuJHnPdMWNtbW?usp=sharing) for a spin. The T4 GPU is sponsored by a community GPU grant from Huggingface. Thanks a lot! """ gr.Markdown(dedent(_)) chatbot = gr.Chatbot() with gr.Row(): with gr.Column(scale=4): with gr.Column(scale=12): user_input = gr.Textbox(show_label=False, placeholder="Input...",).style( container=False) RETRY_FLAG = gr.Checkbox(value=False, visible=False) with gr.Column(min_width=32, scale=1): with gr.Row(): submitBtn = gr.Button("Submit", variant="primary") deleteBtn = gr.Button("Delete last turn", variant="secondary") retryBtn = gr.Button("Regenerate", variant="secondary") with gr.Column(scale=1): emptyBtn = gr.Button("Clear History") max_length = gr.Slider(0, 32768, value=8192/2, step=1.0, label="Maximum length", interactive=True) top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True) temperature = gr.Slider(0.01, 1, value=0.95, step=0.01, label="Temperature", interactive=True) history = gr.State([]) past_key_values = gr.State(None) user_input.submit(predict, [RETRY_FLAG, user_input, chatbot, max_length, top_p, temperature, history, past_key_values], [chatbot, history, past_key_values], show_progress=True) submitBtn.click(predict, [RETRY_FLAG, user_input, chatbot, max_length, top_p, temperature, history, past_key_values], [chatbot, history, past_key_values], show_progress=True, api_name="predict") submitBtn.click(reset_user_input, [], [user_input]) emptyBtn.click(reset_state, outputs=[chatbot, history, past_key_values], show_progress=True) retryBtn.click( retry_last_answer, inputs = [user_input, chatbot, max_length, top_p, temperature, history, past_key_values], #outputs = [chatbot, history, last_user_message, user_message] outputs=[chatbot, history, past_key_values] ) deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history]) with gr.Accordion("For Translation API", open=False): input_text = gr.Text() tr_btn = gr.Button("Go", variant="primary") out_text = gr.Text() tr_btn.click(trans_api, [input_text, max_length, top_p, temperature], out_text, show_progress=True, api_name="tr") input_text.submit(trans_api, [input_text, max_length, top_p, temperature], out_text, show_progress=True, api_name="tr") with gr.Accordion("Example inputs", open=True): examples = gr.Examples( examples=[["Explain the plot of Cinderella in a sentence."], ["How long does it take to become proficient in French, and what are the best methods for retaining information?"], ["What are some common mistakes to avoid when writing code?"], ["Build a prompt to generate a beautiful portrait of a horse"], ["Suggest four metaphors to describe the benefits of AI"], ["Write a pop song about leaving home for the sandy beaches."], ["Write a summary demonstrating my ability to do beat-boxing"]], inputs = [user_input], ) # demo.queue().launch(share=False, inbrowser=True) # demo.queue().launch(share=True, inbrowser=True, debug=True) demo.queue().launch(debug=True)