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Runtime error
ffreemt
commited on
Commit
·
68482b0
1
Parent(s):
cd90503
Fix show_progress=full
Browse files
app.py
CHANGED
@@ -1,3 +1,4 @@
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# import gradio as gr
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# model_name = "models/THUDM/chatglm2-6b-int4"
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@@ -6,17 +7,19 @@
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# %%writefile demo-4bit.py
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from textwrap import dedent
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# credit to https://github.com/THUDM/ChatGLM2-6B/blob/main/web_demo.py
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# while mistakes are mine
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from transformers import AutoModel, AutoTokenizer
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import gradio as gr
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import mdtex2html
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from loguru import logger
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-
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model_name = "THUDM/chatglm2-6b-int4"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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@@ -25,20 +28,23 @@ tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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# 4/8 bit
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# model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda()
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import torch
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has_cuda = torch.cuda.is_available()
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# has_cuda = False # force cpu
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if has_cuda:
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model = AutoModel.from_pretrained(
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else:
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model = AutoModel.from_pretrained(
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model = model.eval()
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_ = """Override Chatbot.postprocess"""
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def postprocess(self, y):
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if y is None:
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return []
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@@ -61,15 +67,15 @@ def parse_text(text):
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for i, line in enumerate(lines):
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if "```" in line:
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count += 1
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items = line.split(
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if count % 2 == 1:
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lines[i] = f'<pre><code class="language-{items[-1]}">'
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else:
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lines[i] =
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else:
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if i > 0:
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if count % 2 == 1:
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line = line.replace("`", "\`")
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line = line.replace("<", "<")
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line = line.replace(">", ">")
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line = line.replace(" ", " ")
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@@ -81,23 +87,31 @@ def parse_text(text):
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line = line.replace("(", "(")
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line = line.replace(")", ")")
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line = line.replace("$", "$")
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lines[i] = "<br>"+line
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text = "".join(lines)
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return text
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def predict(
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try:
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chatbot.append((parse_text(input), ""))
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except Exception as exc:
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logger.error(exc)
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chatbot[-1] = (parse_text(input), str(exc))
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yield chatbot, history, past_key_values
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for response, history, past_key_values in model.stream_chat(
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chatbot[-1] = (parse_text(input), parse_text(response))
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yield chatbot, history, past_key_values
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@@ -112,9 +126,9 @@ def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2):
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temperature = 0.01
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try:
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res, _ = model.chat(
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tokenizer,
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input,
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history=[],
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past_key_values=None,
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max_length=max_length,
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top_p=top_p,
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@@ -126,15 +140,16 @@ def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2):
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res = str(exc)
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return res
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-
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def reset_user_input():
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return gr.update(value=
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def reset_state():
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return [], [], None
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# Delete last turn
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def delete_last_turn(chat, history):
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if chat and history:
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@@ -145,53 +160,49 @@ def delete_last_turn(chat, history):
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# Regenerate response
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def retry_last_answer(
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user_input,
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max_length,
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top_p,
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temperature,
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history,
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past_key_values
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):
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if chatbot and history:
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# Removing the previous conversation from chat
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chatbot.pop(-1)
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# Setting up a flag to capture a retry
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RETRY_FLAG = True
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# Getting last message from user
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user_input = history[-1][0]
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# Removing bot response from the history
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history.pop(-1)
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yield from predict(
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RETRY_FLAG,
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user_input,
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chatbot,
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max_length,
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top_p,
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temperature,
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history,
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past_key_values
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with gr.Blocks(title="ChatGLM2-6B-int4", theme=gr.themes.Soft(text_size="sm")) as demo:
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# gr.HTML("""<h1 align="center">ChatGLM2-6B-int4</h1>""")
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gr.HTML(
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with gr.Accordion("Info", open=False):
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_ = """
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## ChatGLM2-6B-int4
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-
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With a GPU, a query takes from a few seconds to a few tens of seconds, dependent on the number of words/characters
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the question and responses contain. The quality of the responses varies quite a bit it seems. Even the same
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question with the same parameters, asked at different times, can result in quite different responses.
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* Low temperature: responses will be more deterministic and focused; High temperature: responses more creative.
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-
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* Suggested temperatures -- translation: up to 0.3; chatting: > 0.4
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-
* Top P controls dynamic vocabulary selection based on context.
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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)
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@@ -204,8 +215,10 @@ with gr.Blocks(title="ChatGLM2-6B-int4", theme=gr.themes.Soft(text_size="sm")) a
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with gr.Row():
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with gr.Column(scale=4):
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with gr.Column(scale=12):
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user_input = gr.Textbox(
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RETRY_FLAG = gr.Checkbox(value=False, visible=False)
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with gr.Column(min_width=32, scale=1):
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with gr.Row():
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retryBtn = gr.Button("Regenerate", variant="secondary")
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with gr.Column(scale=1):
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emptyBtn = gr.Button("Clear History")
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max_length = gr.Slider(
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history = gr.State([])
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past_key_values = gr.State(None)
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user_input.submit(
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submitBtn.click(reset_user_input, [], [user_input])
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emptyBtn.click(
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retryBtn.click(
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retry_last_answer,
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inputs
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with gr.Accordion("Example inputs", open=True):
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etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
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examples = gr.Examples(
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examples=[
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-
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],
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inputs
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examples_per_page=30,
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)
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input_text = gr.Text()
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tr_btn = gr.Button("Go", variant="primary")
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out_text = gr.Text()
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tr_btn.click(
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# demo.queue().launch(share=False, inbrowser=True)
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# demo.queue().launch(share=True, inbrowser=True, debug=True)
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demo.queue().launch(debug=True)
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# pylint: disable=broad-exception-caught, redefined-outer-name, missing-function-docstring, missing-module-docstring, too-many-arguments, line-too-long, invalid-name, redefined-builtin
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# import gradio as gr
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# model_name = "models/THUDM/chatglm2-6b-int4"
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# %%writefile demo-4bit.py
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from textwrap import dedent
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import torch
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import gradio as gr
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import mdtex2html
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from loguru import logger
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# credit to https://github.com/THUDM/ChatGLM2-6B/blob/main/web_demo.py
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# while mistakes are mine
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from transformers import AutoModel, AutoTokenizer
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model_name = "THUDM/chatglm2-6 b"
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model_name = "THUDM/chatglm2-6b-int4"
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RETRY_FLAG = False
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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# 4/8 bit
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# model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda()
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has_cuda = torch.cuda.is_available()
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# has_cuda = False # force cpu
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if has_cuda:
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model = AutoModel.from_pretrained(
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model_name, trust_remote_code=True
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).cuda() # 3.92G
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else:
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model = AutoModel.from_pretrained(
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model_name, trust_remote_code=True
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).half() # .float() .half().float()
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model = model.eval()
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_ = """Override Chatbot.postprocess"""
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def postprocess(self, y):
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if y is None:
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return []
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for i, line in enumerate(lines):
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if "```" in line:
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count += 1
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items = line.split("`")
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if count % 2 == 1:
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lines[i] = f'<pre><code class="language-{items[-1]}">'
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else:
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lines[i] = "<br></code></pre>"
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else:
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if i > 0:
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if count % 2 == 1:
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line = line.replace("`", r"\`")
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line = line.replace("<", "<")
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line = line.replace(">", ">")
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line = line.replace(" ", " ")
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line = line.replace("(", "(")
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line = line.replace(")", ")")
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line = line.replace("$", "$")
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lines[i] = "<br>" + line
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text = "".join(lines)
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return text
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def predict(
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RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values
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):
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try:
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chatbot.append((parse_text(input), ""))
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except Exception as exc:
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logger.error(exc)
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chatbot[-1] = (parse_text(input), str(exc))
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yield chatbot, history, past_key_values
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+
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for response, history, past_key_values in model.stream_chat(
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tokenizer,
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input,
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history,
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past_key_values=past_key_values,
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return_past_key_values=True,
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max_length=max_length,
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top_p=top_p,
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temperature=temperature,
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):
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chatbot[-1] = (parse_text(input), parse_text(response))
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yield chatbot, history, past_key_values
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temperature = 0.01
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try:
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res, _ = model.chat(
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tokenizer,
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input,
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history=[],
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past_key_values=None,
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max_length=max_length,
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top_p=top_p,
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res = str(exc)
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return res
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+
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def reset_user_input():
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return gr.update(value="")
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def reset_state():
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return [], [], None
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# Delete last turn
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def delete_last_turn(chat, history):
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if chat and history:
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# Regenerate response
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def retry_last_answer(
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user_input, chatbot, max_length, top_p, temperature, history, past_key_values
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):
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if chatbot and history:
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# Removing the previous conversation from chat
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chatbot.pop(-1)
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+
# Setting up a flag to capture a retry
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RETRY_FLAG = True
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# Getting last message from user
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user_input = history[-1][0]
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+
# Removing bot response from the history
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history.pop(-1)
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yield from predict(
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RETRY_FLAG,
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user_input,
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chatbot,
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+
max_length,
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+
top_p,
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+
temperature,
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history,
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past_key_values,
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)
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+
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with gr.Blocks(title="ChatGLM2-6B-int4", theme=gr.themes.Soft(text_size="sm")) as demo:
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# gr.HTML("""<h1 align="center">ChatGLM2-6B-int4</h1>""")
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gr.HTML(
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"""<center><a href="https://huggingface.co/spaces/mikeee/chatglm2-6b-4bit?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>To avoid the queue and for faster inference Duplicate this Space and upgrade to GPU</center>"""
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)
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with gr.Accordion("Info", open=False):
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_ = """
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## ChatGLM2-6B-int4
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+
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+
With a GPU, a query takes from a few seconds to a few tens of seconds, dependent on the number of words/characters
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the question and responses contain. The quality of the responses varies quite a bit it seems. Even the same
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question with the same parameters, asked at different times, can result in quite different responses.
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200 |
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* Low temperature: responses will be more deterministic and focused; High temperature: responses more creative.
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+
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* Suggested temperatures -- translation: up to 0.3; chatting: > 0.4
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204 |
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+
* Top P controls dynamic vocabulary selection based on context.
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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)
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with gr.Row():
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with gr.Column(scale=4):
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with gr.Column(scale=12):
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user_input = gr.Textbox(
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show_label=False,
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placeholder="Input...",
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).style(container=False)
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RETRY_FLAG = gr.Checkbox(value=False, visible=False)
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with gr.Column(min_width=32, scale=1):
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with gr.Row():
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retryBtn = gr.Button("Regenerate", variant="secondary")
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with gr.Column(scale=1):
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emptyBtn = gr.Button("Clear History")
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max_length = gr.Slider(
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0,
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32768,
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value=8192 / 2,
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step=1.0,
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label="Maximum length",
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interactive=True,
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)
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top_p = gr.Slider(
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0, 1, value=0.85, step=0.01, label="Top P", interactive=True
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)
|
241 |
+
temperature = gr.Slider(
|
242 |
+
0.01, 1, value=0.95, step=0.01, label="Temperature", interactive=True
|
243 |
+
)
|
244 |
|
245 |
history = gr.State([])
|
246 |
past_key_values = gr.State(None)
|
247 |
|
248 |
+
user_input.submit(
|
249 |
+
predict,
|
250 |
+
[
|
251 |
+
RETRY_FLAG,
|
252 |
+
user_input,
|
253 |
+
chatbot,
|
254 |
+
max_length,
|
255 |
+
top_p,
|
256 |
+
temperature,
|
257 |
+
history,
|
258 |
+
past_key_values,
|
259 |
+
],
|
260 |
+
[chatbot, history, past_key_values],
|
261 |
+
show_progress="full",
|
262 |
+
)
|
263 |
+
submitBtn.click(
|
264 |
+
predict,
|
265 |
+
[
|
266 |
+
RETRY_FLAG,
|
267 |
+
user_input,
|
268 |
+
chatbot,
|
269 |
+
max_length,
|
270 |
+
top_p,
|
271 |
+
temperature,
|
272 |
+
history,
|
273 |
+
past_key_values,
|
274 |
+
],
|
275 |
+
[chatbot, history, past_key_values],
|
276 |
+
show_progress="full",
|
277 |
+
api_name="predict",
|
278 |
+
)
|
279 |
submitBtn.click(reset_user_input, [], [user_input])
|
280 |
|
281 |
+
emptyBtn.click(
|
282 |
+
reset_state, outputs=[chatbot, history, past_key_values], show_progress="full"
|
283 |
+
)
|
284 |
|
285 |
retryBtn.click(
|
286 |
+
retry_last_answer,
|
287 |
+
inputs=[
|
288 |
+
user_input,
|
289 |
+
chatbot,
|
290 |
+
max_length,
|
291 |
+
top_p,
|
292 |
+
temperature,
|
293 |
+
history,
|
294 |
+
past_key_values,
|
295 |
+
],
|
296 |
+
# outputs = [chatbot, history, last_user_message, user_message]
|
297 |
+
outputs=[chatbot, history, past_key_values],
|
298 |
+
)
|
299 |
+
deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history])
|
300 |
|
301 |
with gr.Accordion("Example inputs", open=True):
|
302 |
etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
|
303 |
examples = gr.Examples(
|
304 |
examples=[
|
305 |
+
["Explain the plot of Cinderella in a sentence."],
|
306 |
+
[
|
307 |
+
"How long does it take to become proficient in French, and what are the best methods for retaining information?"
|
308 |
+
],
|
309 |
+
["What are some common mistakes to avoid when writing code?"],
|
310 |
+
["Build a prompt to generate a beautiful portrait of a horse"],
|
311 |
+
["Suggest four metaphors to describe the benefits of AI"],
|
312 |
+
["Write a pop song about leaving home for the sandy beaches."],
|
313 |
+
["Write a summary demonstrating my ability to tame lions"],
|
314 |
+
["鲁迅和周树人什么关系"],
|
315 |
+
["从前有一头牛,这头牛后面有什么?"],
|
316 |
+
["正无穷大加一大于正无穷大吗?"],
|
317 |
+
["正无穷大加正无穷大大于正无穷大吗?"],
|
318 |
+
["-2的平方根等于什么"],
|
319 |
+
["树上有5只鸟,猎人开枪打死了一只。树上还有几只鸟?"],
|
320 |
+
["树上有11只鸟,猎人开枪打死了一只。树上还有几只鸟?提示:需考虑鸟可能受惊吓飞走。"],
|
321 |
+
["鲁迅和周树人什么关系 用英文回答"],
|
322 |
+
["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"],
|
323 |
+
[f"{etext} 翻成中文,列出3个版本"],
|
324 |
+
[f"{etext} \n 翻成中文,保留原意,但使用文学性的语言。不要写解释。列出3个版本"],
|
325 |
+
["js 判断一个数是不是质数"],
|
326 |
+
["js 实现python 的 range(10)"],
|
327 |
+
["js 实现python 的 [*(range(10)]"],
|
328 |
+
["假定 1 + 2 = 4, 试求 7 + 8"],
|
329 |
+
["Erkläre die Handlung von Cinderella in einem Satz."],
|
330 |
+
["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"],
|
331 |
],
|
332 |
+
inputs=[user_input],
|
333 |
examples_per_page=30,
|
334 |
)
|
335 |
|
|
|
337 |
input_text = gr.Text()
|
338 |
tr_btn = gr.Button("Go", variant="primary")
|
339 |
out_text = gr.Text()
|
340 |
+
tr_btn.click(
|
341 |
+
trans_api,
|
342 |
+
[input_text, max_length, top_p, temperature],
|
343 |
+
out_text,
|
344 |
+
show_progress="full",
|
345 |
+
api_name="tr",
|
346 |
+
)
|
347 |
+
input_text.submit(
|
348 |
+
trans_api,
|
349 |
+
[input_text, max_length, top_p, temperature],
|
350 |
+
out_text,
|
351 |
+
show_progress="full",
|
352 |
+
api_name="tr",
|
353 |
+
)
|
354 |
+
|
355 |
# demo.queue().launch(share=False, inbrowser=True)
|
356 |
# demo.queue().launch(share=True, inbrowser=True, debug=True)
|
357 |
|
358 |
+
demo.queue().launch(debug=True)
|