├── requirements.txt ├── images ├── local.png ├── logo.png ├── online.png ├── seq_image.png ├── architecture.png └── compass_support.svg ├── 模型评测结果 └── 20240314_094433 │ ├── results │ └── InternLM2-Chat-7B-SFT-SMC-V1.0 │ │ └── smart_home.json │ ├── summary │ ├── summary_20240314_094433.csv │ └── summary_20240314_094433.txt │ └── configs │ └── 20240314_094433.py ├── gradio_demo.py ├── app.py ├── .gitignore ├── internlm2_chat_7b_qlora_smarthome_e30.py ├── README.md └── LICENSE /requirements.txt: -------------------------------------------------------------------------------- 1 | gradio==4.21.0 2 | requests -------------------------------------------------------------------------------- /images/local.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jujunchen/SmartHomeCLLM/HEAD/images/local.png -------------------------------------------------------------------------------- /images/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jujunchen/SmartHomeCLLM/HEAD/images/logo.png -------------------------------------------------------------------------------- /images/online.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jujunchen/SmartHomeCLLM/HEAD/images/online.png -------------------------------------------------------------------------------- /images/seq_image.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jujunchen/SmartHomeCLLM/HEAD/images/seq_image.png -------------------------------------------------------------------------------- /images/architecture.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jujunchen/SmartHomeCLLM/HEAD/images/architecture.png -------------------------------------------------------------------------------- /模型评测结果/20240314_094433/results/InternLM2-Chat-7B-SFT-SMC-V1.0/smart_home.json: -------------------------------------------------------------------------------- 1 | { 2 | "accuracy": 71.91419141914191 3 | } -------------------------------------------------------------------------------- /模型评测结果/20240314_094433/summary/summary_20240314_094433.csv: -------------------------------------------------------------------------------- 1 | dataset,version,metric,mode,InternLM2-Chat-7B-SFT-SMC-V1.0 2 | smart_home,7c7205,accuracy,gen,71.91 3 | -------------------------------------------------------------------------------- /gradio_demo.py: -------------------------------------------------------------------------------- 1 | import gradio as gr 2 | import torch 3 | from transformers import AutoTokenizer, AutoModelForCausalLM 4 | 5 | # 修改为模型路径 6 | model_name_or_path = "./Greentown_SmartHomeCLLM/" 7 | 8 | 9 | tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) 10 | model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='auto') 11 | model = model.eval() 12 | 13 | system_prompt = """ 14 | 你现在是一个智能家居AI助手, 能够从用户上下文提取出用户的动作(action), 设备(device), 空间(space), 接口(api), 回复(reponse), 场景(scene), 设备id(device_id), 场景id(scene_id), 没有的字段返回空; 15 | 设备数据从这里匹配: (device_id=1,客厅,射灯);(device_id=4,客厅,筒灯);(device_id=13,客厅,灯带);(device_id=11,客厅,窗帘); 16 | 场景数据从这里匹配: (scene_id=1,回家);(scene_id=2,离家);(scene_id=4,洗浴);(scene_id=5,睡眠); 17 | """ 18 | 19 | messages = [(system_prompt, '')] 20 | 21 | def process_input(input_text): 22 | response, history = model.chat(tokenizer, input_text, history=messages) 23 | return response 24 | 25 | # 创建 gradio 接口 26 | iface = gr.Interface( 27 | fn=process_input, 28 | inputs=gr.Textbox(label="输入指令"), 29 | outputs=gr.JSON(), 30 | title="绿城智能家居指令大模型Demo" 31 | ) 32 | iface.launch() -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import gradio as gr 2 | import requests 3 | 4 | def process_input(user_input, user_input2): 5 | try: 6 | # 构造API请求 7 | url = "http://183.129.211.90:22323/smartlife/chat" 8 | headers = {"Content-Type": "application/json"} # 根据你的API需求调整 9 | data = {"messages": user_input} # 构造请求体，这里假设API需要一个名为"param"的参数 10 | 11 | # 发起请求并获取响应 12 | response = requests.post(url, headers=headers, json=data) 13 | 14 | # 处理响应并返回结果 15 | if response.status_code == 200: 16 | return response.json() 17 | else: 18 | return {"error": "Failed to fetch data from API"} 19 | except Exception as e: 20 | return {"error": str(e)} 21 | 22 | # 创建Gradio界面 23 | iface = gr.Interface( 24 | fn=process_input, 25 | inputs=gr.Dropdown(["打开主卧灯", "关闭主卧灯"], label="选择指令"), 26 | outputs=gr.JSON(), 27 | article= """1、为了部署到openXLab，我们开发了跟演示视频不同的Demo，该Demo也对接了实验室的IOT平台，能真实控制实验室设备，该Demo仅支持两个指令。 28 |
29 | 2、直播地址能够实时看到设备控制情况，由于摄像头问题，请在手机端打开观看，视频流有20秒左右延迟，请评委耐心等待一下...有时候会打不开，麻烦评委多尝试一下... 30 |
直播地址：http://test.aliali.vip/video 31 | """, 32 | title="绿城智能家居指令大模型Demo" 33 | ) 34 | 35 | iface.launch() # 启动界面 -------------------------------------------------------------------------------- /模型评测结果/20240314_094433/summary/summary_20240314_094433.txt: -------------------------------------------------------------------------------- 1 | 20240314_094433 2 | tabulate format 3 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 4 | dataset version metric mode InternLM2-Chat-7B-SFT-SMC-V1.0 5 | ---------- --------- -------- ------ -------------------------------- 6 | smart_home 7c7205 accuracy gen 71.91 7 | $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ 8 | 9 | -------------------------------------------------------------------------------------------------------------------------------- THIS IS A DIVIDER -------------------------------------------------------------------------------------------------------------------------------- 10 | 11 | csv format 12 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 13 | dataset,version,metric,mode,InternLM2-Chat-7B-SFT-SMC-V1.0 14 | smart_home,7c7205,accuracy,gen,71.91 15 | $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ 16 | 17 | -------------------------------------------------------------------------------------------------------------------------------- THIS IS A DIVIDER -------------------------------------------------------------------------------------------------------------------------------- 18 | 19 | raw format 20 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 21 | ------------------------------- 22 | Model: InternLM2-Chat-7B-SFT-SMC-V1.0 23 | smart_home: {'accuracy': 71.91419141914191} 24 | $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ 25 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | -------------------------------------------------------------------------------- /images/compass_support.svg: -------------------------------------------------------------------------------- 1 | OpenCompass: SupportOpenCompassSupport 2 | -------------------------------------------------------------------------------- /模型评测结果/20240314_094433/configs/20240314_094433.py: -------------------------------------------------------------------------------- 1 | datasets=[ 2 | dict(abbr='smart_home', 3 | eval_cfg=dict( 4 | evaluator=dict( 5 | type='opencompass.datasets.SmartHomeEvaluator'), 6 | pred_role='BOT'), 7 | infer_cfg=dict( 8 | inferencer=dict( 9 | type='opencompass.openicl.icl_inferencer.GenInferencer'), 10 | prompt_template=dict( 11 | template=dict( 12 | begin=[ 13 | dict(fallback_role='HUMAN', 14 | prompt='你现在是一个智能家居AI助手, 能够从用户上下文提取出用户的动作(action), 设备(device), 空间(space), 接口(api), 回复(reponse), 场景(scene), 设备id(device_id), 场景id(scene_id), 没有的字段返回空;-device_id从这里匹配: (device_id=1,射灯);(device_id=2,筒灯);(device_id=3,灯带);(device_id=4,窗帘);-scene_id从这里匹配: (scene_id=1,回家);(scene_id=2,离家);(scene_id=4,洗浴);(scene_id=5,睡眠);\n', 15 | role='SYSTEM'), 16 | ], 17 | round=[ 18 | dict(prompt='{question}', 19 | role='HUMAN'), 20 | dict(prompt='{answer}', 21 | role='BOT'), 22 | ]), 23 | type='opencompass.openicl.icl_prompt_template.PromptTemplate'), 24 | retriever=dict( 25 | type='opencompass.openicl.icl_retriever.ZeroRetriever')), 26 | path='./data/smart_home/conversations-test.jsonl', 27 | reader_cfg=dict( 28 | input_columns=[ 29 | 'question', 30 | ], 31 | output_column='answer', 32 | test_split='test'), 33 | type='opencompass.datasets.SmartHomeDataset'), 34 | ] 35 | internlm2_chat_7b=dict( 36 | abbr='InternLM2-Chat-7B-SFT-SMC-V1.0', 37 | api_addr='http://0.0.0.0:8080', 38 | batch_size=16, 39 | gen_config=dict( 40 | max_new_tokens=300, 41 | temperature=0.1, 42 | top_k=40, 43 | top_p=1), 44 | max_out_len=300, 45 | max_seq_len=2048, 46 | run_cfg=dict( 47 | num_gpus=4, 48 | num_procs=1), 49 | type='opencompass.models.turbomind_api.TurboMindAPIModel') 50 | models=[ 51 | dict(abbr='InternLM2-Chat-7B-SFT-SMC-V1.0', 52 | api_addr='http://0.0.0.0:8080', 53 | batch_size=16, 54 | gen_config=dict( 55 | max_new_tokens=300, 56 | temperature=0.1, 57 | top_k=40, 58 | top_p=1), 59 | max_out_len=300, 60 | max_seq_len=2048, 61 | run_cfg=dict( 62 | num_gpus=4, 63 | num_procs=1), 64 | type='opencompass.models.turbomind_api.TurboMindAPIModel'), 65 | ] 66 | sh_datasets=[ 67 | dict(abbr='smart_home', 68 | eval_cfg=dict( 69 | evaluator=dict( 70 | type='opencompass.datasets.SmartHomeEvaluator'), 71 | pred_role='BOT'), 72 | infer_cfg=dict( 73 | inferencer=dict( 74 | type='opencompass.openicl.icl_inferencer.GenInferencer'), 75 | prompt_template=dict( 76 | template=dict( 77 | begin=[ 78 | dict(fallback_role='HUMAN', 79 | prompt='你现在是一个智能家居AI助手, 能够从用户上下文提取出用户的动作(action), 设备(device), 空间(space), 接口(api), 回复(reponse), 场景(scene), 设备id(device_id), 场景id(scene_id), 没有的字段返回空;-device_id从这里匹配: (device_id=1,射灯);(device_id=2,筒灯);(device_id=3,灯带);(device_id=4,窗帘);-scene_id从这里匹配: (scene_id=1,回家);(scene_id=2,离家);(scene_id=4,洗浴);(scene_id=5,睡眠);\n', 80 | role='SYSTEM'), 81 | ], 82 | round=[ 83 | dict(prompt='{question}', 84 | role='HUMAN'), 85 | dict(prompt='{answer}', 86 | role='BOT'), 87 | ]), 88 | type='opencompass.openicl.icl_prompt_template.PromptTemplate'), 89 | retriever=dict( 90 | type='opencompass.openicl.icl_retriever.ZeroRetriever')), 91 | path='./data/smart_home/conversations-test.jsonl', 92 | reader_cfg=dict( 93 | input_columns=[ 94 | 'question', 95 | ], 96 | output_column='answer', 97 | test_split='test'), 98 | type='opencompass.datasets.SmartHomeDataset'), 99 | ] 100 | work_dir='./outputs/default/20240314_094433' -------------------------------------------------------------------------------- /internlm2_chat_7b_qlora_smarthome_e30.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | from datasets import load_dataset 4 | from mmengine.dataset import DefaultSampler 5 | from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook, 6 | LoggerHook, ParamSchedulerHook) 7 | from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR 8 | from peft import LoraConfig 9 | from torch.optim import AdamW 10 | from transformers import (AutoModelForCausalLM, AutoTokenizer, 11 | BitsAndBytesConfig) 12 | 13 | from xtuner.dataset import process_hf_dataset 14 | from xtuner.dataset.collate_fns import default_collate_fn 15 | from xtuner.dataset.map_fns import template_map_fn_factory 16 | from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook, 17 | VarlenAttnArgsToMessageHubHook) 18 | from xtuner.engine.runner import TrainLoop 19 | from xtuner.model import SupervisedFinetune 20 | from xtuner.utils import PROMPT_TEMPLATE 21 | 22 | ####################################################################### 23 | # PART 1 Settings # 24 | ####################################################################### 25 | # Model 26 | pretrained_model_name_or_path = '/app/models/internlm2-chat-7b' #预训练模型路径 27 | use_varlen_attn = False 28 | 29 | # Data 30 | data_path = './dataset/conversations-train.jsonl' # 训练数据集路径 31 | prompt_template = PROMPT_TEMPLATE.internlm2_chat 32 | max_length = 2048 33 | pack_to_max_length = False 34 | 35 | # Scheduler & Optimizer 36 | batch_size = 15 # 批次大小 37 | accumulative_counts = 16 38 | dataloader_num_workers = 0 #数据加载进程数，和CPU有关 39 | max_epochs = 30 # 训练轮次 40 | optim_type = AdamW # 优化器类型 41 | lr = 2e-4 # 学习率 42 | betas = (0.9, 0.999) 43 | weight_decay = 0 # 正则化系数 44 | max_norm = 1 # grad clip 45 | warmup_ratio = 0.03 # 预热比例 46 | 47 | # Save 48 | save_steps = 500 49 | save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited) 50 | 51 | # Evaluate the generation performance during the training 52 | evaluation_freq = 500 53 | SYSTEM = '你现在是一个智能家居AI助手' 54 | evaluation_inputs = [ 55 | '客厅灯带开一下','需要关闭餐厅的筒灯','我回家了','你好','你是谁' 56 | ] 57 | 58 | ####################################################################### 59 | # PART 2 Model & Tokenizer # 60 | ####################################################################### 61 | tokenizer = dict( 62 | type=AutoTokenizer.from_pretrained, 63 | pretrained_model_name_or_path=pretrained_model_name_or_path, 64 | trust_remote_code=True, 65 | padding_side='right') 66 | 67 | model = dict( 68 | type=SupervisedFinetune, 69 | use_varlen_attn=use_varlen_attn, 70 | llm=dict( 71 | type=AutoModelForCausalLM.from_pretrained, 72 | pretrained_model_name_or_path=pretrained_model_name_or_path, 73 | trust_remote_code=True, 74 | torch_dtype=torch.float16, 75 | quantization_config=dict( 76 | type=BitsAndBytesConfig, 77 | load_in_4bit=True, 78 | load_in_8bit=False, 79 | llm_int8_threshold=6.0, 80 | llm_int8_has_fp16_weight=False, 81 | bnb_4bit_compute_dtype=torch.float16, 82 | bnb_4bit_use_double_quant=True, 83 | bnb_4bit_quant_type='nf4')), 84 | lora=dict( 85 | type=LoraConfig, 86 | r=64, 87 | lora_alpha=16, 88 | lora_dropout=0.1, 89 | bias='none', 90 | task_type='CAUSAL_LM')) 91 | 92 | ####################################################################### 93 | # PART 3 Dataset & Dataloader # 94 | ####################################################################### 95 | train_dataset = dict( 96 | type=process_hf_dataset, 97 | dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)), 98 | tokenizer=tokenizer, 99 | max_length=max_length, 100 | dataset_map_fn=None, 101 | template_map_fn=dict( 102 | type=template_map_fn_factory, template=prompt_template), 103 | remove_unused_columns=True, 104 | shuffle_before_pack=True, 105 | pack_to_max_length=pack_to_max_length, 106 | use_varlen_attn=use_varlen_attn) 107 | 108 | train_dataloader = dict( 109 | batch_size=batch_size, 110 | num_workers=dataloader_num_workers, 111 | dataset=train_dataset, 112 | sampler=dict(type=DefaultSampler, shuffle=True), 113 | collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn)) 114 | 115 | ####################################################################### 116 | # PART 4 Scheduler & Optimizer # 117 | ####################################################################### 118 | # optimizer 119 | optim_wrapper = dict( 120 | type=AmpOptimWrapper, 121 | optimizer=dict( 122 | type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay), 123 | clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False), 124 | accumulative_counts=accumulative_counts, 125 | loss_scale='dynamic', 126 | dtype='float16') 127 | 128 | # learning policy 129 | # More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501 130 | param_scheduler = [ 131 | dict( 132 | type=LinearLR, 133 | start_factor=1e-5, 134 | by_epoch=True, 135 | begin=0, 136 | end=warmup_ratio * max_epochs, 137 | convert_to_iter_based=True), 138 | dict( 139 | type=CosineAnnealingLR, 140 | eta_min=0.0, 141 | by_epoch=True, 142 | begin=warmup_ratio * max_epochs, 143 | end=max_epochs, 144 | convert_to_iter_based=True) 145 | ] 146 | 147 | # train, val, test setting 148 | train_cfg = dict(type=TrainLoop, max_epochs=max_epochs) 149 | 150 | ####################################################################### 151 | # PART 5 Runtime # 152 | ####################################################################### 153 | # Log the dialogue periodically during the training process, optional 154 | custom_hooks = [ 155 | dict(type=DatasetInfoHook, tokenizer=tokenizer), 156 | dict( 157 | type=EvaluateChatHook, 158 | tokenizer=tokenizer, 159 | every_n_iters=evaluation_freq, 160 | evaluation_inputs=evaluation_inputs, 161 | system=SYSTEM, 162 | prompt_template=prompt_template) 163 | ] 164 | 165 | if use_varlen_attn: 166 | custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)] 167 | 168 | # configure default hooks 169 | default_hooks = dict( 170 | # record the time of every iteration. 171 | timer=dict(type=IterTimerHook), 172 | # print log every 10 iterations. 173 | logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10), 174 | # enable the parameter scheduler. 175 | param_scheduler=dict(type=ParamSchedulerHook), 176 | # save checkpoint per `save_steps`. 177 | checkpoint=dict( 178 | type=CheckpointHook, 179 | by_epoch=False, 180 | interval=save_steps, 181 | max_keep_ckpts=save_total_limit), 182 | # set sampler seed in distributed evrionment. 183 | sampler_seed=dict(type=DistSamplerSeedHook), 184 | ) 185 | 186 | # configure environment 187 | env_cfg = dict( 188 | # whether to enable cudnn benchmark 189 | cudnn_benchmark=False, 190 | # set multi process parameters 191 | mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), 192 | # set distributed parameters 193 | dist_cfg=dict(backend='nccl'), 194 | ) 195 | 196 | # set visualizer 197 | visualizer = None 198 | 199 | # set log level 200 | log_level = 'INFO' 201 | 202 | # load from which checkpoint 203 | load_from = None 204 | 205 | # whether to resume training from the loaded checkpoint 206 | resume = False 207 | 208 | # Defaults to use random seed and disable `deterministic` 209 | randomness = dict(seed=None, deterministic=False) 210 | 211 | # set log processor 212 | log_processor = dict(by_epoch=False) 213 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 绿城智能家居指令大模型 2 |
3 | 4 | 5 |
6 | SmartHomeCLLM 7 |
8 | 9 | [![license][license-image]][license-url] 10 | [![evaluation][evaluation-image]][evaluation-url] 11 | [![LMDeploy][LMDeploy-image]][LMDeploy-url] 12 | [![XTuner][XTuner-image]][XTuner-url] 13 | 14 | 15 | [![OpenXLab_Model][OpenXLab_Model-image]][OpenXLab_Model-url] 16 | [![OpenXLab_App][OpenXLab_App-image]][OpenXLab_App-url] 17 | 18 | [🤔Reporting Issues][Issues-url] 19 | 20 | 21 | [license-image]: https://img.shields.io/badge/license-GPL%203.0-green 22 | [evaluation-image]: ./images/compass_support.svg 23 | [OpenXLab_Model-image]: https://cdn-static.openxlab.org.cn/header/openxlab_models.svg 24 | [LMDeploy-image]: https://img.shields.io/badge/LMDeploy-Support-blue 25 | [XTuner-image]: https://img.shields.io/badge/XTuner-Support-blue 26 | [OpenXLab_App-image]: https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg 27 | 28 | [license-url]: ./LICENSE 29 | [evaluation-url]: https://github.com/internLM/OpenCompass/ 30 | [OpenXLab_Model-url]:https://openxlab.org.cn/models/detail/Greentown/Greentown_SmartHomeCLLM 31 | [LMDeploy-url]: https://github.com/internLM/LMDeploy 32 | [XTuner-url]: https://github.com/internLM/XTuner 33 | [OpenXLab_App-url]: https://openxlab.org.cn/apps/detail/Greentown/Greentown_SmartHomeCLLM 34 | [Issues-url]: https://github.com/jujunchen/SmartHomeCLLM/issues 35 |
36 | 37 | ## 📝目录 38 | 39 | - [📖 简介](#-简介) 40 | - [🚀 News](#-news) 41 | - [📦 Model Zoo](#-ModelZoo) 42 | - [⚡️ 性能](#-性能) 43 | - [🛠️ 使用方法](#-使用方法) 44 | + [文件说明](#文件说明) 45 | + [环境搭建](#环境搭建) 46 | + [运行Demo](#运行Demo) 47 | + [微调](#微调) 48 | + [部署](#部署) 49 | + [评测](#评测) 50 | - [技术报告](#技术报告) 51 | - [致谢](#致谢) 52 | - [开源许可证](#开源许可证) 53 | 54 | 55 | ## 📖 简介 56 | 智能家居指令大模型，由绿城未来数智通过数十万条智能家居控制指令基于internLM2_chat_7b微调训练而成，大模型能够根据用户上下文识别出文本中的指令、设备名称、空间名称、接口、回复、场景、设备id、场景id，值等，并返回JSON结构，可供IOT平台进一步的指令解析、执行、控制。配合prompt工程，可以实现设备、场景数据的动态识别。 57 | 58 | ![绿城智能家居指令大模型架构图](./images/architecture.png) 59 | 60 | ![智能家居指令控制参考时序图](./images/seq_image.png) 61 | 62 | ## 🚀 News 63 | - 2024.03.12 项目第一版上线 64 | ## 📦 ModelZoo 65 | | 模型名称 | OpenXLab | Release Date | 66 | | :----: | :----: | :----: | 67 | | InternLM2-Chat-7B-SFT-SMC-V1.0 | [![OpenXLab_Model][OpenXLab_Model-image]][OpenXLab_Model-url] | 2024.03.12 | 68 | ## ⚡️ 性能 69 | 使用OpenCompass进行测试，结果如下： 70 | |dataset| version| metric| mode| InternLM2-Chat-7B-SFT-SMC-V1.0| 71 | | :----: | :----: | :----: | :----: | :----: | 72 | |smart_home| 7c7205| accuracy| gen| 71.91| 73 | ## 🛠️ 使用方法 74 | ### 文件说明 75 | - app.py: openxlab 应用代码，通过api接口调用部署在测试服务器上的前端应用接口 76 | - requirements.txt: openxlab 应用依赖包列表 77 | - gradio_demo.py: 使用gradio搭建的前端应用，可以直接调用大模型，用于展示模型效果，内置了设备数据、场景数据。要体验大模型的指令识别能力，可以运行该文件。 78 | - internlm2_chat_7b_qlora_smarthome_e30.py: 微调训练配置文件 79 | - 模型评测结果: 包含模型评测结果文件 80 | 81 | ### 环境搭建 82 | 1. clone 本项目 83 | ```bash 84 | git clone https://github.com/jujunchen/SmartHomeCLLM.git 85 | cd SmartHomeCLLM 86 | ``` 87 | 2. 创建虚拟环境 88 | 89 | ```bash 90 | conda create --name smartHomeCLLM python=3.10 -y 91 | conda activate smartHomeCLLM 92 | ``` 93 | 3. 安装依赖 94 | ```bash 95 | pip install gradio==4.21.0 96 | pip install requests 97 | pip install torch 98 | pip install transformers 99 | pip install sentencepiece 100 | pip install einops 101 | pip install accelerate 102 | ``` 103 | 4. 模型下载 104 | ```bash 105 | # HTTP下载： 106 | git lfs install 107 | git clone https://code.openxlab.org.cn/Greentown/Greentown_SmartHomeCLLM.git 108 | ``` 109 | ### 运行Demo 110 | 1. 在线Demo体验 111 | > - 为了部署到openXLab，我们开发了跟演示视频不同的Demo，该Demo也对接了实验室的IOT平台，能真实控制实验室设备 112 | > 113 | > - 直播地址能够实时看到设备控制情况，由于摄像头问题，*请在手机端打开观看，视频流有20秒左右延迟，请评委耐心等待一下...有时候会打不开，麻烦评委多尝试一下...* 114 | 115 | 演示地址：https://openxlab.org.cn/apps/detail/Greentown/Greentown_SmartHomeCLLM 116 | 直播地址：http://test.aliali.vip/video 117 | 118 | ![在线Demo体验](./images/online.png) 119 | 120 | 2. 本地运行 121 | > 本地运行Demo，能够体验大模型的指令识别能力 122 | ```bash 123 | # 修改为模型路径 124 | model_name_or_path = "./Greentown_SmartHomeCLLM/" 125 | 126 | # 运行demo 127 | python gradio_demo.py 128 | ``` 129 | ![本地运行Demo](./images/local.png) 130 | ### 微调 131 | 1. xtuner 安装 132 | ```bash 133 | pip install -U 'xtuner[deepspeed]' 134 | ``` 135 | 2. 数据集准备 136 | 数据格式应该是如下这样的json格式，保存为.jsonl文件 137 | ```bash 138 | [ 139 | { 140 | "conversation": [ 141 | { 142 | "system": "你现在是一个智能家居AI助手", 143 | "input": "打开主卧灯。", 144 | "output": "{\"type\":\"iot_device\",\"response\":\"已为您打开主卧灯\",\"api\":\"\",\"params\":{\"device_id\":\"11\",\"device\":\"灯\",\"space\":\"主卧\",\"action\":\"P2_0xCF\",\"value\":\"100\"}}" 145 | } 146 | ] 147 | }, 148 | { 149 | "conversation": [ 150 | { 151 | "system": "你现在是一个智能家居AI助手", 152 | "input": "关闭主卧灯。", 153 | "output": "{\"type\":\"iot_device\",\"response\":\"已为您关闭主卧灯\",\"api\":\"\",\"params\":{\"device_id\":\"11\",\"device\":\"灯\",\"space\":\"主卧\",\"action\":\"P2_0xCF\",\"value\":\"100\"}}" 154 | } 155 | ] 156 | } 157 | ] 158 | 159 | # output 内json格式说明 160 | # type: 类型 161 | # response: 响应 162 | # api: api 163 | # device_id: 控制的设备id 164 | # device: 设备名称 165 | # space: 设备所在空间 166 | # action: 指令 167 | # value: 指令值 168 | ``` 169 | 3. 修改配置文件 170 | 修改internlm2_chat_7b_qlora_smarthome_e30.py文件 171 | ```python 172 | #预训练模型路径，为您下载的模型路径 173 | pretrained_model_name_or_path = '/app/models/Greentown_SmartHomeCLLM/' 174 | 175 | # 训练数据集路径，为您自己的数据集路径 176 | data_path = './dataset/conversations-train.jsonl' 177 | ``` 178 | 4. 开始训练 179 | ```bash 180 | # 多卡训练 181 | NPROC_PER_NODE=8 xtuner train ./internlm2_chat_7b_qlora_smarthome_e30.py --deepspeed deepspeed_zero2 182 | 183 | # 单卡 184 | xtuner train ./internlm2_chat_7b_qlora_smarthome_e30.py --deepspeed deepspeed_zero2 185 | ``` 186 | 5. 将训练得到的PTH模型转换为HuggingFace模型 187 | ```bash 188 | xtuner convert pth_to_hf ./internlm2_chat_7b_qlora_smarthome_e30.py ./work_dirs/internlm2_chat_7b_qlora_smarthome_e30/iter_850.pth ./hf/ 189 | ``` 190 | 6. 将HuggingFace 合并到大模型 191 | ```bash 192 | # 参数说明 193 | # NAME_OR_PATH_TO_LLM 原始模型存放的位置 194 | # NAME_OR_PATH_TO_ADAPTER Hugging Face格式存放的位置 195 | # SAVE_PATH 新模型存放的位置 196 | xtuner convert merge \ 197 | $NAME_OR_PATH_TO_LLM \ 198 | $NAME_OR_PATH_TO_ADAPTER \ 199 | $SAVE_PATH \ 200 | --max-shard-size 2GB 201 | 202 | 203 | xtuner convert merge ./Greentown_SmartHomeCLLM/ ./hf/ ./merged/ --max-shard-size 2GB 204 | ``` 205 | 7. 参考运行Demo配置 206 | ### 部署 207 | 1. 将模型转换为lmdeploy TurboMind 的格式 208 | ```bash 209 | # 转换模型（FastTransformer格式） TurboMind 210 | lmdeploy convert internlm2-chat-7b ./Greentown_SmartHomeCLLM 211 | ``` 212 | 2. 部署为API服务 213 | ```bash 214 | # ApiServer+Turbomind api_server => AsyncEngine => TurboMind 215 | lmdeploy serve api_server ./workspace \ 216 | --server_name 0.0.0.0 \ 217 | --server_port 23333 \ 218 | --instance_num 64 \ 219 | --tp 1 220 | ``` 221 | ### 评测 222 | 本模型需要使用自定义数据集进行评测，需要对openCompass新增数据集。 223 | 1. 下载openCompass 224 | ```bash 225 | git clone https://github.com/open-compass/opencompass.git 226 | ``` 227 | 2. 修改配置 228 | 229 | opencompass/datasets/__init__.py 新增 230 | ```bash 231 | from .smart_home import * 232 | ``` 233 | 新增 opencompass/datasets/smart_home.py 234 | ```python 235 | import csv 236 | import json 237 | import os.path as osp 238 | 239 | from datasets import Dataset, DatasetDict 240 | 241 | from opencompass.openicl.icl_evaluator import BaseEvaluator 242 | from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET 243 | from opencompass.utils.text_postprocessors import general_postprocess 244 | 245 | from .base import BaseDataset 246 | 247 | 248 | @LOAD_DATASET.register_module() 249 | class SmartHomeDataset(BaseDataset): 250 | 251 | @staticmethod 252 | def load(path: str): 253 | dataset = DatasetDict() 254 | data_list = list() 255 | with open(path, 'r') as f: 256 | for line in f: 257 | if line.strip(): 258 | data_list.append(json.loads(line.strip())) 259 | 260 | dataset["test"] = Dataset.from_list(data_list) 261 | dataset["train"] = Dataset.from_list(data_list) 262 | return dataset 263 | 264 | 265 | @ICL_EVALUATORS.register_module() 266 | class SmartHomeEvaluator(BaseEvaluator): 267 | 268 | def score(self, predictions, references): 269 | score = 0 270 | 271 | # 确保predictions和references长度一致 272 | if len(predictions) == len(references): 273 | for pred, ref in zip(predictions, references): 274 | try: 275 | # 尝试解析predictions中的JSON部分 276 | pred_json = json.loads(pred) 277 | if pred_json: 278 | # 解析references中的JSON 279 | ref_json = json.loads(ref) 280 | 281 | # 检查字段名是否一致 282 | if all(key in ref_json for key in pred_json): 283 | score += 1 284 | # print(f"Match found for prediction: {pred}") 285 | else: 286 | print(f"No match found for prediction: {pred}") 287 | except json.JSONDecodeError: 288 | print(f"Invalid JSON in prediction: {pred}") 289 | else: 290 | print("The lengths of predictions and references lists are not equal.") 291 | return {'accuracy': 100 * score / len(predictions), 'details': []} 292 | ``` 293 | 新增 configs/eval_internlm2_chat_lmdeploy_apiserver.py 294 | ```python 295 | from mmengine.config import read_base 296 | from opencompass.models.turbomind_api import TurboMindAPIModel 297 | 298 | with read_base(): 299 | from .datasets.smart_home.sh_gen import sh_datasets 300 | 301 | datasets = [*sh_datasets] 302 | 303 | 304 | internlm2_chat_7b = dict( 305 | type=TurboMindAPIModel, 306 | abbr='InternLM2-Chat-7B-SFT-SMC-V1.0', 307 | api_addr='http://0.0.0.0:8080', 308 | gen_config=dict(top_k=40, top_p=1, 309 | temperature=0.1, 310 | max_new_tokens=300), 311 | max_out_len=300, 312 | max_seq_len=2048, 313 | batch_size=16, 314 | run_cfg=dict(num_gpus=4, num_procs=1) 315 | ) 316 | 317 | models = [internlm2_chat_7b] 318 | ``` 319 | 新增configs/datasets/smart_home/sh_gen.py 320 | ```python 321 | from mmengine.config import read_base 322 | 323 | with read_base(): 324 | from .sh_gen_e78df3 import sh_datasets # noqa: F401, F403 325 | ``` 326 | 新增configs/datasets/smart_home/sh_gen_e78df3.py 327 | ```python 328 | from opencompass.openicl.icl_prompt_template import PromptTemplate 329 | from opencompass.openicl.icl_retriever import ZeroRetriever 330 | from opencompass.openicl.icl_inferencer import GenInferencer 331 | from opencompass.openicl.icl_evaluator import AccEvaluator 332 | from opencompass.datasets import SmartHomeDataset, SmartHomeEvaluator 333 | 334 | sh_reader_cfg = dict( 335 | input_columns=["question"], 336 | output_column="answer", 337 | test_split="test") 338 | 339 | sh_infer_cfg = dict( 340 | prompt_template=dict( 341 | type=PromptTemplate, 342 | template=dict( 343 | begin=[ 344 | dict(role='SYSTEM', fallback_role='HUMAN', prompt='你现在是一个智能家居AI助手\n'), 345 | ], 346 | round=[ 347 | dict( 348 | role="HUMAN", 349 | prompt= 350 | "{question}" 351 | ), 352 | dict( 353 | role="BOT", 354 | prompt= 355 | "{answer}" 356 | ) 357 | ], ), 358 | ), 359 | retriever=dict(type=ZeroRetriever), 360 | inferencer=dict(type=GenInferencer), 361 | ) 362 | 363 | sh_eval_cfg = dict( 364 | evaluator=dict(type=SmartHomeEvaluator), 365 | pred_role="BOT", 366 | ) 367 | 368 | sh_datasets = [ 369 | dict( 370 | abbr="smart_home", 371 | type=SmartHomeDataset, 372 | path='./data/smart_home/conversations-test.jsonl', 373 | reader_cfg=sh_reader_cfg, 374 | infer_cfg=sh_infer_cfg, 375 | eval_cfg=sh_eval_cfg) 376 | ] 377 | 378 | ``` 379 | 3. 开始评测 380 | ```bash 381 | # 开启debug模式 382 | python run.py configs/eval_internlm2_chat_lmdeploy_apiserver.py --debug 383 | ``` 384 | 385 | ## 技术报告 386 | https://gtportal.feishu.cn/docx/OEewdxp8Jo4mITxcIZ7cGrOUnYd 387 | 388 | ## 致谢 389 | 感谢以下项目： 390 |
391 | 392 | ***感谢上海人工智能实验室组织的 书生·浦语实战营 学习活动*** 393 | 394 | [**InternLM-tutorial**](https://github.com/InternLM/tutorial)、[**Xtuner**](https://github.com/InternLM/xtuner)、[**LMDeploy**](https://github.com/InternLM/lmdeploy)、[**OpenCompass**](https://github.com/open-compass/opencompass)、[**OpenXLab**](https://openxlab.org.cn/home)、[**Lagent**](https://github.com/InternLM/lagent)、[**AgentLego**](https://github.com/InternLM/agentlego) 395 |
396 | 397 | ## 开源许可证 398 | 该项目采用 [GPL 3.0 开源许可证](https://github.com/jujunchen/SmartHomeCLLM/blob/main/LICENSE) 同时，请遵守所使用的模型与数据集的许可证。 -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------