llama3.1
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app_Llama3_Gradio2.py
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177
app_Llama3_Gradio2.py
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import gradio as gr
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import os
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import torch
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from transformers import GemmaTokenizer, AutoModelForCausalLM
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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from threading import Thread
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DESCRIPTION = '''
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<div>
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<h1 style="text-align: center;">EmoLLM Llama3 心理咨询室 V4.0</h1>
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<p align="center">
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<a href="https://github.com/SmartFlowAI/EmoLLM/">
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<img src="https://st-app-center-006861-9746-jlroxvg.openxlab.space/media/cda6c1a05dc8ba5b19ad3e7a24920fdf3750c917751202385a6dbc51.png" alt="Logo" width="20%">
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</a>
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</p>
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<div align="center">
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<!-- PROJECT SHIELDS -->
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[![OpenXLab_Model][OpenXLab_Model-image]][OpenXLab_Model-url]
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<h2 style="text-align: center;"> EmoLLM是一系列能够支持 理解用户-支持用户-帮助用户 心理健康辅导链路的 心理健康大模型 ，欢迎大家star~⭐⭐</h2>
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<p>https://github.com/SmartFlowAI/EmoLLM</p>
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</div>
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</div>
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[OpenXLab_Model-image]: https://cdn-static.openxlab.org.cn/header/openxlab_models.svg
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[OpenXLab_Model-url]: https://openxlab.org.cn/models/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0
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'''
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LICENSE = """
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<p align="center"> Built with Meta Llama 3 </>
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"""
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PLACEHOLDER = """
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<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
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</div>
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"""
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css = """
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h1 {
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text-align: center;
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display: block;
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}
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<!--
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#duplicate-button {
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margin: auto;
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color: white;
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background: #1565c0;
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border-radius: 100vh;
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}
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-->
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"""
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# download internlm2 to the base_path directory using git tool
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base_path = './EmoLLM-Llama3-8B-Instruct3.0'
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os.system(f'git clone https://code.openxlab.org.cn/chg0901/EmoLLM-Llama3-8B-Instruct3.0.git {base_path}')
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os.system(f'cd {base_path} && git lfs pull')
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# Load the tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained(base_path,trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(base_path,trust_remote_code=True, device_map="auto", torch_dtype=torch.float16).eval() # to("cuda:0")
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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def chat_llama3_8b(message: str,
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history: list,
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temperature: float,
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max_new_tokens: int,
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top_p: float
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) -> str:
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"""
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Generate a streaming response using the llama3-8b model.
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Args:
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message (str): The input message.
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history (list): The conversation history used by ChatInterface.
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temperature (float): The temperature for generating the response.
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max_new_tokens (int): The maximum number of new tokens to generate.
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Returns:
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str: The generated response.
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"""
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conversation = []
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for user, assistant in history:
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conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
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conversation.append({"role": "user", "content": message})
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input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
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streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = dict(
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input_ids= input_ids,
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streamer=streamer,
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max_new_tokens=max_new_tokens,
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do_sample=True,
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temperature=temperature,
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top_p = top_p,
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eos_token_id=terminators,
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)
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# This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.
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if temperature == 0:
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generate_kwargs['do_sample'] = False
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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t.start()
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outputs = []
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for text in streamer:
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outputs.append(text)
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yield "".join(outputs)
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# Gradio block
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chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='EmoLLM Chat')
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with gr.Blocks(fill_height=True, css=css) as demo:
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gr.Markdown(DESCRIPTION)
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# gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
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gr.ChatInterface(
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fn=chat_llama3_8b,
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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(minimum=0,
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maximum=1,
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step=0.1,
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value=0.95,
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label="Temperature",
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render=False),
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gr.Slider(minimum=128,
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maximum=4096,
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step=1,
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value=4096,
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label="Max new tokens",
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render=False ),
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gr.Slider(minimum=0.0,
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maximum=1,
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step=0.01,
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value=0.8,
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label="Top P",
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render=False ),
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# gr.Slider(minimum=128,
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# maximum=4096,
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# step=1,
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# value=512,
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# label="Max new tokens",
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# render=False ),
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],
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examples=[
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['请介绍你自己。'],
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['我觉得我在学校的学习压力好大啊，虽然我真的很喜欢我的专业，但最近总是担心自己无法达到自己的期望，这让我有点焦虑。'],
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['我最近总觉得自己在感情上陷入了困境，我喜欢上了我的朋友，但又害怕表达出来会破坏我们现在的关系...'],
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['我感觉自己像是被困在一个无尽的循环中。每天醒来都感到身体沉重，对日常活动提不起兴趣，工作、锻炼甚至是我曾经喜欢的事物都让我觉得厌倦'],
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['最近工作压力特别大，还有一些家庭矛盾']
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],
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cache_examples=False,
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)
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gr.Markdown(LICENSE)
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if __name__ == "__main__":
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demo.launch()
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# Copyright (c) OpenMMLab. All rights reserved.
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"""
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Ref: https://github.com/InternLM/xtuner/edit/main/xtuner/configs/internlm/internlm2_5_chat_7b/internlm2_5_chat_7b_full_finetune_custom_dataset_e1.py
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Data format:
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[
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{
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"conversation": [
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{
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"system": "",
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"input": "xxx",
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"output": "xxx"
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},
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{
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"input": "xxx",
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"output": "xxx"
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}
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]
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},
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...
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]
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Please refer to https://github.com/InternLM/xtuner/blob/main/docs/en/user_guides/dataset_format.md for details.
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""" # noqa: E501
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from datasets import load_dataset
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from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
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LoggerHook, ParamSchedulerHook)
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from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR
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from torch.optim import AdamW
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from torch.utils.data import BatchSampler
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from xtuner.dataset import process_hf_dataset
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from xtuner.dataset.collate_fns import default_collate_fn
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from xtuner.dataset.map_fns import template_map_fn_factory
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from xtuner.dataset.samplers import InternRepoSampler
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from xtuner.engine import (DatasetInfoHook, EvaluateChatHook, ThroughputHook,
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VarlenAttnArgsToMessageHubHook)
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from xtuner.engine.runner import TrainLoop
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from xtuner.model import SupervisedFinetune
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from xtuner.utils import PROMPT_TEMPLATE
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#######################################################################
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# PART 1 Settings #
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#######################################################################
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# Model
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pretrained_model_name_or_path = '/root/group_share/Hong/Meta-Llama-3___1-8B-Instruct'
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use_varlen_attn = True
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# Data
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data_files = ['/root/EmoLLM/datasets/multi_turn_dataset_2.json']
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prompt_template = PROMPT_TEMPLATE.internlm2_chat
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# max_length = 32768
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max_length = int(32768/4) ## A100*2
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pack_to_max_length = True
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# parallel
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sequence_parallel_size = 1
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# Scheduler & Optimizer
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# batch size per device, set to 1 if `use_varlen_attn` = True
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# To clarify, enlarging the batch size essentially enlarges the `max_length`.
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# For example, doubling the max length is tantamount to doubling the batch size
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batch_size = 1
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accumulative_counts = 1 # 1bs * 1acc * 64gpu = 64 batchsize
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accumulative_counts *= sequence_parallel_size
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dataloader_num_workers = 4
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max_epochs = 3
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optim_type = AdamW
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lr = 4e-5
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betas = (0.9, 0.95)
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weight_decay = 0.01
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max_norm = 1 # grad clip
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warm_up_ratio = 0.025
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# Save
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save_steps = 500
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save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)
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# Evaluate the generation performance during the training
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evaluation_freq = 500
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SYSTEM = "你由EmoLLM团队打造的中文领域心理健康助手, 是一个研究过无数具有心理健康问题的病人与心理健康医生对话的心理专家, 在心理方面拥有广博的知识储备和丰富的研究咨询经验，你旨在通过专业心理咨询, 协助来访者完成心理诊断。请充分利用专业心理学知识与咨询技术, 一步步帮助来访者解决心理问题, 接下来你将只使用中文来回答和咨询问题。"
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evaluation_inputs = [
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# '躲在云朵下面就不怕淋雨了', # ruozhi train
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# '李白如果告语文书侵权的话能赔不少钱吧', # ruozhi test
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# '雨天，我走进水坑里，不小心踩碎了天空。', # ruozhi test
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'请介绍你自己', # self cognition
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'你好',
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'我今天心情不好，感觉不开心，很烦。',
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'我最近总是感到很焦虑，尤其是在学业上。我有个特别崇拜的同学，他好像在各方面都比我优秀，我总觉得自己怎么努力也追不上他，这让我压力特别大。',
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]
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# "这是一句富有想象力和幽默感的表达。在现实生活中，躲在云朵下面并不能避免淋雨，因为云朵实际上是由水蒸气凝结形成的，而雨就是由这些水滴凝结而成的。\n\n这样的表达可能是在夸张和幽默的语境中，通过一种天马行空的方式来表达逃避现实或者寻找避难的愿望。在文学或口语表达中，常常会运用夸张和幽默来传达情感或者引起共鸣。",
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# "如果李白（701年2月28日—762年12月）真的能就侵权诉讼获得赔偿，那确实可能是一笔不小的金额。然而，这种想法主要是一种有趣的假设，因为现实中有几个关键因素使这种情况不可能发生：\n\n1. **时间差异**：李白生活在唐朝，距今大约1200多年前。那 个时代的法律体系与现代的知识产权法律截然不同，当时没有现代意义上的版权法。\n\n2. **版权法的适用范围**：即使在现代，版权 法也有一定的时效限制。在大多数国家，版权保护通常在作者去世后一定年数内有效（如我国是作者去世后50年）。李白去世已超过1250年，因此其作品已经进入公共领域，任何人都可以自由使用而无需支付版权费用。\n\n3. **历史与现实的区别**：历史人物无法在现代 法律体系中提起诉讼，因为他们不再是活跃的法律主体。\n\n所以，虽然这是一个有趣的想法，但在现实中，李白或其他古代作者无法因其作品被现代出版物使用而获得赔偿。",
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# "这个描述似乎是一个修辞手法，比喻性地描述了雨天的场景。在这个描述中，说“我走进水坑里，不小心踩碎了天空”，实际上并非字面意义上的发生，而是一种用词语来比喻雨天的场景。\n\n通常情况下，当雨水落在水坑或者蓄水池时，水面会泛起涟漪或者波纹，可能会反射天空的颜色或者天空的倒影。因此，这句话可能是通过“踩碎了天空”的说法来比喻雨天时踩进水坑的情景，描述雨水落在水坑中形成的波纹或者涟漪，产生了一种倒映天空的效果。\n\n这种形象化的表达方式可能是为了更生动地描述一个平常的场景，赋予它一些诗意或者意境。",
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#######################################################################
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# PART 2 Model & Tokenizer #
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#######################################################################
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tokenizer = dict(
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type=AutoTokenizer.from_pretrained,
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pretrained_model_name_or_path=pretrained_model_name_or_path,
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trust_remote_code=True,
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padding_side='right')
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model = dict(
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type=SupervisedFinetune,
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use_varlen_attn=use_varlen_attn,
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llm=dict(
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type=AutoModelForCausalLM.from_pretrained,
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pretrained_model_name_or_path=pretrained_model_name_or_path,
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trust_remote_code=True))
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#######################################################################
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# PART 3 Dataset & Dataloader #
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#######################################################################
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train_dataset = dict(
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type=process_hf_dataset,
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use_varlen_attn=use_varlen_attn,
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dataset=dict(type=load_dataset, path='json', data_files=data_files),
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tokenizer=tokenizer,
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max_length=max_length,
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dataset_map_fn=None,
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template_map_fn=dict(
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type=template_map_fn_factory, template=prompt_template),
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remove_unused_columns=True,
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shuffle_before_pack=True,
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pack_to_max_length=pack_to_max_length)
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train_dataloader = dict(
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batch_size=batch_size,
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num_workers=dataloader_num_workers,
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dataset=train_dataset,
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sampler=dict(type=InternRepoSampler, shuffle=True, seed=1024),
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batch_sampler=dict(
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type=BatchSampler, drop_last=True, batch_size=batch_size),
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collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn))
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#######################################################################
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# PART 4 Scheduler & Optimizer #
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#######################################################################
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# optimizer
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optim_wrapper = dict(
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type=AmpOptimWrapper,
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optimizer=dict(
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type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
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clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
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accumulative_counts=accumulative_counts,
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loss_scale='dynamic',
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)
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# learning policy
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# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1 / 40,
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by_epoch=True,
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begin=0,
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end=warm_up_ratio * max_epochs,
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convert_to_iter_based=True),
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dict(
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type=CosineAnnealingLR,
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eta_min=lr * 0.15,
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by_epoch=True,
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begin=warm_up_ratio * max_epochs,
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end=max_epochs,
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convert_to_iter_based=True)
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]
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# train, val, test setting
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train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)
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#######################################################################
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# PART 5 Runtime #
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#######################################################################
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# Log the dialogue periodically during the training process, optional
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custom_hooks = [
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dict(
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type=DatasetInfoHook, tokenizer=tokenizer,
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is_intern_repo_dataset=True),
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dict(
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type=EvaluateChatHook,
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tokenizer=tokenizer,
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every_n_iters=evaluation_freq,
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evaluation_inputs=evaluation_inputs,
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system=SYSTEM,
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prompt_template=prompt_template),
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dict(type=ThroughputHook)
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]
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if use_varlen_attn:
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custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)]
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# configure default hooks
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default_hooks = dict(
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# record the time of every iteration.
|
||||
timer=dict(type=IterTimerHook),
|
||||
# print log every 100 iterations.
|
||||
logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=1),
|
||||
# enable the parameter scheduler.
|
||||
param_scheduler=dict(type=ParamSchedulerHook),
|
||||
# save checkpoint per `save_steps`.
|
||||
checkpoint=dict(
|
||||
type=CheckpointHook,
|
||||
by_epoch=False,
|
||||
interval=save_steps,
|
||||
max_keep_ckpts=save_total_limit),
|
||||
# set sampler seed in distributed evrionment.
|
||||
sampler_seed=dict(type=DistSamplerSeedHook),
|
||||
)
|
||||
|
||||
# configure environment
|
||||
env_cfg = dict(
|
||||
# whether to enable cudnn benchmark
|
||||
cudnn_benchmark=False,
|
||||
# set multi process parameters
|
||||
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
|
||||
# set distributed parameters
|
||||
dist_cfg=dict(backend='nccl'),
|
||||
)
|
||||
|
||||
# set visualizer
|
||||
visualizer = None
|
||||
|
||||
# set log level
|
||||
log_level = 'INFO'
|
||||
|
||||
# load from which checkpoint
|
||||
load_from = None
|
||||
|
||||
# whether to resume training from the loaded checkpoint
|
||||
resume = False
|
||||
|
||||
# Defaults to use random seed and disable `deterministic`
|
||||
randomness = dict(seed=None, deterministic=False)
|
||||
|
||||
log_processor = dict(
|
||||
by_epoch=False,
|
||||
window_size=1,
|
||||
mean_pattern=r'.*(loss|time|data_time|grad_norm|tflops).*')
|
||||
Loading…
Reference in New Issue
Block a user