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幻想照进事实，微软果然不愧是微软，开源了贾维斯 (J.A.R.V.I.S.) 人工智能助理零碎，贾维斯 (jarvis) 全称为 Just A Rather Very Intelligent System（只是一个相当聪慧的人工智能零碎），它能够帮忙钢铁侠托尼斯塔克实现各种工作和挑战，包含管制和治理托尼的机甲配备，提供实时情报和数据分析，帮忙托尼做出决策等等。
现在，咱们也能够领有本人的贾维斯人工智能助理，老本仅仅是一块 RTX3090 显卡。
贾维斯 (Jarvis) 的环境配置
个别状况下，深度学习畛域绝对支流的入门级别显卡是 2070 或者 3070，而 3090 能够算是生产级深度学习显卡的天花板了：
再往上走就是工业级别的 A 系列和 V 系列显卡，显存是一个硬指标，因为须要加载本地的大模型，尽管能够改代码对模型加载进行“阉割”，但性能上必定也会有肯定的损失。如果没有 3090，也能够组两块 3060 12G 的并行，显存尽管能够达标，但算力和综合性能抵不过 3090。
确保本地具备足以撑持贾维斯 (Jarvis) 的硬件环境之后，老规矩，克隆我的项目：
git clone https://github.com/microsoft/JARVIS.git随后进入我的项目目录：
cd JARVIS批改我的项目的配置文件 server/config.yaml:
openai:
key: your_personal_key # gradio, your_personal_key
huggingface:
cookie: # required for huggingface inference
local: # ignore: just for development
endpoint: http://localhost:8003
dev: false
debug: false
log_file: logs/debug.log
model: text-davinci-003 # text-davinci-003
use_completion: true
inference_mode: hybrid # local, huggingface or hybrid
local_deployment: minimal # no, minimal, standard or full
num_candidate_models: 5
max_description_length: 100
proxy:
httpserver:
host: localhost
port: 8004
modelserver:
host: localhost
port: 8005
logit_bias:
parse_task: 0.1
choose_model: 5这里次要批改三个配置即可，别离是 openaikey，huggingface 官网的 cookie 令牌，以及 OpenAI 的 model，默认应用的模型是 text-davinci-003。
批改实现后，官网举荐应用虚拟环境 conda，Python 版本 3.8，私认为这里齐全没有任何必要应用虚拟环境，间接上 Python3.10 即可，接着装置依赖：
pip3 install -r requirements.txt我的项目依赖库如下：
git+https://github.com/huggingface/diffusers.git@8c530fc2f6a76a2aefb6b285dce6df1675092ac6#egg=diffusers
git+https://github.com/huggingface/transformers@c612628045822f909020f7eb6784c79700813eda#egg=transformers
git+https://github.com/patrickvonplaten/controlnet_aux@78efc716868a7f5669c288233d65b471f542ce40#egg=controlnet_aux
tiktoken==0.3.3
pydub==0.25.1
espnet==202301
espnet_model_zoo==0.1.7
flask==2.2.3
flask_cors==3.0.10
waitress==2.1.2
datasets==2.11.0
asteroid==0.6.0
speechbrain==0.5.14
timm==0.6.13
typeguard==2.13.3
accelerate==0.18.0
pytesseract==0.3.10
gradio==3.24.1这里 web 端接口是用 Flask2.2 高版本搭建的，但奇怪的是微软并未应用 Flask 新版本的异步个性。
装置实现之后，进入模型目录：
cd models下载模型和数据集：
sh download.sh这里肯定要做好心理准备，因为模型就曾经占用海量的硬盘空间了，数据集更是不用多说，所有文件均来自 huggingface:
models="
nlpconnect/vit-gpt2-image-captioning
lllyasviel/ControlNet
runwayml/stable-diffusion-v1-5
CompVis/stable-diffusion-v1-4
stabilityai/stable-diffusion-2-1
Salesforce/blip-image-captioning-large
damo-vilab/text-to-video-ms-1.7b
microsoft/speecht5_asr
facebook/maskformer-swin-large-ade
microsoft/biogpt
facebook/esm2_t12_35M_UR50D
microsoft/trocr-base-printed
microsoft/trocr-base-handwritten
JorisCos/DCCRNet_Libri1Mix_enhsingle_16k
espnet/kan-bayashi_ljspeech_vits
facebook/detr-resnet-101
microsoft/speecht5_tts
microsoft/speecht5_hifigan
microsoft/speecht5_vc
facebook/timesformer-base-finetuned-k400
runwayml/stable-diffusion-v1-5
superb/wav2vec2-base-superb-ks
openai/whisper-base
Intel/dpt-large
microsoft/beit-base-patch16-224-pt22k-ft22k
facebook/detr-resnet-50-panoptic
facebook/detr-resnet-50
openai/clip-vit-large-patch14
google/owlvit-base-patch32
microsoft/DialoGPT-medium
bert-base-uncased
Jean-Baptiste/camembert-ner
deepset/roberta-base-squad2
facebook/bart-large-cnn
google/tapas-base-finetuned-wtq
distilbert-base-uncased-finetuned-sst-2-english
gpt2
mrm8488/t5-base-finetuned-question-generation-ap
Jean-Baptiste/camembert-ner
t5-base
impira/layoutlm-document-qa
ydshieh/vit-gpt2-coco-en
dandelin/vilt-b32-finetuned-vqa
lambdalabs/sd-image-variations-diffusers
facebook/timesformer-base-finetuned-k400
facebook/maskformer-swin-base-coco
Intel/dpt-hybrid-midas
lllyasviel/sd-controlnet-canny
lllyasviel/sd-controlnet-depth
lllyasviel/sd-controlnet-hed
lllyasviel/sd-controlnet-mlsd
lllyasviel/sd-controlnet-openpose
lllyasviel/sd-controlnet-scribble
lllyasviel/sd-controlnet-seg
"
# CURRENT_DIR=$(cd `dirname $0`; pwd)
CURRENT_DIR=$(pwd)
for model in $models;
do
echo "----- Downloading from https://huggingface.co/"$model"-----"
if [-d "$model"]; then
# cd $model && git reset --hard && git pull && git lfs pull
cd $model && git pull && git lfs pull
cd $CURRENT_DIR
else
# git clone 蕴含了 lfs
git clone https://huggingface.co/$model $model
fi
done
datasets="Matthijs/cmu-arctic-xvectors"
for dataset in $datasets;
do
echo "----- Downloading from https://huggingface.co/datasets/"$dataset"-----"
if [-d "$dataset"]; then
cd $dataset && git pull && git lfs pull
cd $CURRENT_DIR
else
git clone https://huggingface.co/datasets/$dataset $dataset
fi
done也能够思考拆成两个 shell，开多过程下载，速度会快很多。
但事实上，真的，别下了，文件属实过于微小，这玩意儿真的不是普通人能耍起来的，当然抉择不下载本地模型和数据集也能运行，请看下文。
漫长的下载流程完结之后，贾维斯 (Jarvis) 就配置好了。
运行贾维斯(Jarvis)
如果您抉择下载了所有的模型和数据集（拜服您是条汉子），终端内启动服务：
python models_server.py --config config.yaml随后会在零碎的 8004 端口启动一个 Flask 服务过程，而后发动 Http 申请即可运行贾维斯(Jarvis)：
curl --location 'http://localhost:8004/hugginggpt' \
--header 'Content-Type: application/json' \
--data '{"messages": [
{
"role": "user",
"content": "please generate a video based on \"Spiderman is surfing\""
}
]
}'这个的意思是让贾维斯 (Jarvis) 生成一段“蜘蛛侠在冲浪”的视频。
当然了，以笔者的硬件环境，是不可能跑起来的，所以能够对加载的模型适当“阉割”，在 models\_server.py 文件的 81 行左右：
other_pipes = {
"nlpconnect/vit-gpt2-image-captioning":{"model": VisionEncoderDecoderModel.from_pretrained(f"{local_fold}/nlpconnect/vit-gpt2-image-captioning"),
"feature_extractor": ViTImageProcessor.from_pretrained(f"{local_fold}/nlpconnect/vit-gpt2-image-captioning"),
"tokenizer": AutoTokenizer.from_pretrained(f"{local_fold}/nlpconnect/vit-gpt2-image-captioning"),
"device": "cuda:0"
},
"Salesforce/blip-image-captioning-large": {"model": BlipForConditionalGeneration.from_pretrained(f"{local_fold}/Salesforce/blip-image-captioning-large"),
"processor": BlipProcessor.from_pretrained(f"{local_fold}/Salesforce/blip-image-captioning-large"),
"device": "cuda:0"
},
"damo-vilab/text-to-video-ms-1.7b": {"model": DiffusionPipeline.from_pretrained(f"{local_fold}/damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16"),
"device": "cuda:0"
},
"facebook/maskformer-swin-large-ade": {"model": MaskFormerForInstanceSegmentation.from_pretrained(f"{local_fold}/facebook/maskformer-swin-large-ade"),
"feature_extractor" : AutoFeatureExtractor.from_pretrained("facebook/maskformer-swin-large-ade"),
"device": "cuda:0"
},
"microsoft/trocr-base-printed": {"processor": TrOCRProcessor.from_pretrained(f"{local_fold}/microsoft/trocr-base-printed"),
"model": VisionEncoderDecoderModel.from_pretrained(f"{local_fold}/microsoft/trocr-base-printed"),
"device": "cuda:0"
},
"microsoft/trocr-base-handwritten": {"processor": TrOCRProcessor.from_pretrained(f"{local_fold}/microsoft/trocr-base-handwritten"),
"model": VisionEncoderDecoderModel.from_pretrained(f"{local_fold}/microsoft/trocr-base-handwritten"),
"device": "cuda:0"
},
"JorisCos/DCCRNet_Libri1Mix_enhsingle_16k": {"model": BaseModel.from_pretrained("JorisCos/DCCRNet_Libri1Mix_enhsingle_16k"),
"device": "cuda:0"
},
"espnet/kan-bayashi_ljspeech_vits": {"model": Text2Speech.from_pretrained(f"espnet/kan-bayashi_ljspeech_vits"),
"device": "cuda:0"
},
"lambdalabs/sd-image-variations-diffusers": {"model": DiffusionPipeline.from_pretrained(f"{local_fold}/lambdalabs/sd-image-variations-diffusers"), #torch_dtype=torch.float16
"device": "cuda:0"
},
"CompVis/stable-diffusion-v1-4": {"model": DiffusionPipeline.from_pretrained(f"{local_fold}/CompVis/stable-diffusion-v1-4"),
"device": "cuda:0"
},
"stabilityai/stable-diffusion-2-1": {"model": DiffusionPipeline.from_pretrained(f"{local_fold}/stabilityai/stable-diffusion-2-1"),
"device": "cuda:0"
},
"runwayml/stable-diffusion-v1-5": {"model": DiffusionPipeline.from_pretrained(f"{local_fold}/runwayml/stable-diffusion-v1-5"),
"device": "cuda:0"
},
"microsoft/speecht5_tts":{"processor": SpeechT5Processor.from_pretrained(f"{local_fold}/microsoft/speecht5_tts"),
"model": SpeechT5ForTextToSpeech.from_pretrained(f"{local_fold}/microsoft/speecht5_tts"),
"vocoder": SpeechT5HifiGan.from_pretrained(f"{local_fold}/microsoft/speecht5_hifigan"),
"embeddings_dataset": load_dataset(f"{local_fold}/Matthijs/cmu-arctic-xvectors", split="validation"),
"device": "cuda:0"
},
"speechbrain/mtl-mimic-voicebank": {"model": WaveformEnhancement.from_hparams(source="speechbrain/mtl-mimic-voicebank", savedir="models/mtl-mimic-voicebank"),
"device": "cuda:0"
},
"microsoft/speecht5_vc":{"processor": SpeechT5Processor.from_pretrained(f"{local_fold}/microsoft/speecht5_vc"),
"model": SpeechT5ForSpeechToSpeech.from_pretrained(f"{local_fold}/microsoft/speecht5_vc"),
"vocoder": SpeechT5HifiGan.from_pretrained(f"{local_fold}/microsoft/speecht5_hifigan"),
"embeddings_dataset": load_dataset(f"{local_fold}/Matthijs/cmu-arctic-xvectors", split="validation"),
"device": "cuda:0"
},
"julien-c/wine-quality": {"model": joblib.load(cached_download(hf_hub_url("julien-c/wine-quality", "sklearn_model.joblib")))
},
"facebook/timesformer-base-finetuned-k400": {"processor": AutoImageProcessor.from_pretrained(f"{local_fold}/facebook/timesformer-base-finetuned-k400"),
"model": TimesformerForVideoClassification.from_pretrained(f"{local_fold}/facebook/timesformer-base-finetuned-k400"),
"device": "cuda:0"
},
"facebook/maskformer-swin-base-coco": {"feature_extractor": MaskFormerFeatureExtractor.from_pretrained(f"{local_fold}/facebook/maskformer-swin-base-coco"),
"model": MaskFormerForInstanceSegmentation.from_pretrained(f"{local_fold}/facebook/maskformer-swin-base-coco"),
"device": "cuda:0"
},
"Intel/dpt-hybrid-midas": {"model": DPTForDepthEstimation.from_pretrained(f"{local_fold}/Intel/dpt-hybrid-midas", low_cpu_mem_usage=True),
"feature_extractor": DPTFeatureExtractor.from_pretrained(f"{local_fold}/Intel/dpt-hybrid-midas"),
"device": "cuda:0"
}
}把不须要的模型屏蔽即可。
与此同时，还能够不通过本地服务接口，批改 config.yaml 的 inference\_mode: huggingface
随后间接在终端运行 ChatGPT 版本的贾维斯：
cd server
python3 awesome_chat.py --config config.yaml --mode cli运行成果：
Welcome to Jarvis! A collaborative system that consists of an LLM as the controller and numerous expert models as collaborative executors. Jarvis can plan tasks, schedule Hugging Face models, generate friendly responses based on your requests, and help you with many things. Please enter your request (`exit` to exit).
[User]: Given a collection of image A: /examples/cat.jpg, B: /examples/z3.jpg, C: /examples/z1.jpg, please tell me how many zebras in these picture?
[Jarvis]: Based on your request, I have used several models to process the images you provided. For the image A: /examples/cat.jpg, I used the image-to-text model nlpconnect/vit-gpt2-image-captioning to generate the text "a cat sitting on a window sill looking out". Then I used the object-detection model facebook/detr-resnet-50 to generate the image with predicted box '/images/a3dc.jpg', which contains two objects with labels 'potted plant' and 'cat'. For the image B: /examples/z3.jpg, I used the image-to-text model nlpconnect/vit-gpt2-image-captioning to generate the text "two zebras standing in a field of dry grass". Then I used the object-detection model facebook/detr-resnet-50 to generate the image with predicted box '/images/f5df.jpg', which contains three objects with labels 'zebra'. For the image C: /examples/z1.jpg, I used the image-to-text model nlpconnect/vit-gpt2-image-captioning to generate the text "a zebra standing in a field of tall grass". Then I used the object-detection model facebook/detr-resnet-50 to generate the image with predicted box '/images/69c7.jpg', which contains one object with label 'zebra'. Finally, I used the canny-control model canny-control to generate the image '/images/7e18.png' based on the image /examples/savanna.jpg. Therefore, there are four zebras in these pictures. Is there anything else I can help you with?
[User]: Please answer all the named entities in the sentence: Iron Man is a superhero appearing in American comic books published by Marvel Comics. The character was co-created by writer and editor Stan Lee, developed by scripter Larry Lieber, and designed by artists Don Heck and Jack Kirby.
[Jarvis]: Sure, I understand your request. Based on the inference results of the models, I will answer your request step by step. Firstly, I used the token-classification model dslim/bert-base-NER to recognize the named entities in the sentence. The inference result is that the named entities are Iron Man, Stan Lee, Larry Lieber, Don Heck and Jack Kirby. Then, I used the question-answering model deepset/roberta-base-squad2 to answer your request. The inference result is that there is no output since the context cannot be empty. Therefore, I cannot make it. I hope this answer your request. Is there anything else I can help you with?
[User]: Please generate a canny image based on /examples/savanna.jpg
[Jarvis]: Sure. I have generated a canny image based on /examples/savanna.jpg. To do this, I first used the image-to-text model nlpconnect/vit-gpt2-image-captioning to generate the text description of the image, which is "a herd of giraffes and zebras grazing in a field". Then I used the canny-control model to generate the canny image of the original image, which is stored in the path "/images/1ea2.png". Finally, I used the canny-text-to-image model lllyasviel/sd-controlnet-canny to generate the canny image based on the text description and the canny image, which is stored in the path "/images/ba66.png". Do you need anything else?能够了解为 ChatGPT 版本的文字生成图片，里面套了个贾维斯 (Jarvis) 的壳儿，上演成果满分。
结语
总的来说，和微软之前开源的“可视化图形 ChatGPT”一样，此类我的项目的象征意义要远远大于现实意义。贾维斯 (Jarvis) 代表的是大多数技术同仁的独特愿景，对于这类人工智能技术的倒退，能够必定，但因为硬件门槛过高的起因，短期内还不能过于期待。
正文完
