Package: open-gpts
OpenGPTOpenGPTis an open-sourcecloud-nativelargemulti-modal models(LMMs) serving solution.
It is designed to simplify the deployment and management of large language models, on a distributed cluster of GPUs.The content of README.md is just a placeholder to remind me of what I want to do.FeaturesOpenGPT provides the following features to make it easy to deploy and serve large multi-modal models (LMMs) in production:Support for multi-modal modelsScalable architecture for handling high traffic loadsOptimized for low-latency inferenceAutomatic model partitioning and distribution across multiple GPUsCentralized model management and monitoringREST API for easy integration with existing applicationsYou can learn more about OpenGPT’sarchitecture in our documentation.RoadmapYou can view our roadmap with features that are planned, started, and completed on theRoadmap discussioncategory.InstallationInstall the package with pip:pipinstallopen_gptQuickstartimportopen_gptsmodel=open_gpts.create_model('facebook/llama-7b',device='cuda',precision='fp16')prompt="The quick brown fox jumps over the lazy dog."output=model.generate(prompt,max_length=100,temperature=0.9,top_k=50,top_p=0.95,repetition_penalty=1.2,do_sample=True,num_return_sequences=1,)We also provide some advanced features to allow you to host your models cost-effectively:Offloading: you can offload parts of the model to CPU to reduce the cost of inference.Quantization: you can quantize the model to reduce the cost of inference.For more details, please see thedocumentation.Serving ModelsYou can serve your models with OpenGPT. To do so, you can use theservecommand:opengptservefacebook/llama-9b--devicecuda--precisionfp16--port5000This will start a server on port 5000. You can then send requests to the server:importrequestsprompt="The quick brown fox jumps over the lazy dog."response=requests.post("http://localhost:5000/generate",json={"prompt":prompt,"max_length":100,"temperature":0.9,"top_k":50,"top_p":0.95,"repetition_penalty":1.2,"do_sample":True,"num_return_sequences":1,},)# SSE supportfromaiohttp_sse_clientimportclientassse_clientasyncwithsse_client.EventSource('http://localhost:5000/stream/generate?prompt=The+quick+brown+fox+jumps+over+the+lazy+dog.&max_length=100&temperature=0.9&top_k=50&top_p=0.95&repetition_penalty=1.2&do_sample=True&num_return_sequences=1')asevent_source:try:asyncforeventinevent_source:print(event)exceptConnectionError:passNote that the server will only accept requests from the same machine. If you want to accept requests from other machines, you can use the--hostflag to specify the host to bind to.Deploying ModelsYou can also deploy the server to a cloud provider like Jina Cloud or AWS.
To do so, you can usedeploycommand:Jina Cloudopengptdeployfacebook/llama-9b--devicecuda--precisionfp16--providerjina--nameopengpt--replicas2AWSTo deploy to AWS, you need to install extra dependencies:pipinstallopegpt[aws]And you need to specify the region:opengptdeployfacebook/llama-9b--devicecuda--precisionfp16--provideraws--regionus-east-1--nameopengpt--replicas2This will deploy the model to the cloud provider. You can then send requests to the server:importrequestsprompt="The quick brown fox jumps over the lazy dog."response=requests.post("https://opengpt.jina.ai/generate",json={"prompt":prompt,"max_length":100,"temperature":0.9,"top_k":50,"top_p":0.95,"repetition_penalty":1.2,"do_sample":True,"num_return_sequences":1,},)KubernetesTo deploy OpenGPT on your Kubernetes cluster, follow these steps:Install the OpenGPT operator on your Kubernetes cluster using Helm:helminstallopengpt./helm/opengpt--namespaceopengptCreate a custom resource for your GPT model:apiVersion:opengpt.io/v1alpha1kind:GptModelmetadata:name:my-gpt-modelnamespace:opengptspec:modelPath:s3://my-bucket/my-modelmodelName:my-modelmaxBatchSize:16inputShape:-1024-1024-3outputShape:-1024-1024-3Apply the custom resource to your cluster:kubectlapply-fmy-gpt-model.yamlMonitor the status of your GPT model using the OpenGPT dashboard:kubectlport-forward-nopengptsvc/opengpt-dashboard8080:80Accessing models via APIYou can also access the online models via API. To do so, you can use theinference_clientpackage:frominference_clientimportClientclient=Client(token='<your access token>')model=client.get_model('facebook/llama-9b')prompt="The quick brown fox jumps over the lazy dog."output=model.generate(prompt,max_length=100,temperature=0.9,top_k=50,top_p=0.95,repetition_penalty=1.2,do_sample=True,num_return_sequences=1,)By this way, you can access the models without deploying them to your own machine.Advanced UsageModel OffloadingYou can also apply the model offloading techniques (based onFlexTensor) to OpenGPT. To do so, you can use the--offload-percentsflag:opengptservefacebook/llama-9b--devicecuda--precisionfp16--port5000--offload-percents10,90,50,50,0,100This will offload parts of the model to the CPU. You can also use the--offload-strategyflag to specify the offloading strategy:opengptservefacebook/llama-9b--devicecuda--precisionfp16--port5000--offload-strategy"cpu,cpu,cpu,cpu,cpu,cpu"Model QuantizationYou can also apply the model quantization techniques.8-bit quantizationopengptservefacebook/llama-9b--devicecuda--precisionfp16--port5000--quantize8bitFine-tuning ModelsWe currently support fine-tuning models by using thefinetunecommand:opengptfinetunefacebook/llama-9b--datasetwikitext-2--devicecuda--precisionfp16--batch-size32--learning-rate1e-4--epochs10Specifically, we implement the following fine-tuning methods:LLaMA-Adapter: Efficient Fine-tuning of LLaMA: Fine-tuning model to follow instructions within 1 Hour and 1.2M Parameterslow-rank adaptation (LoRA): Low-Rank Adaptation for Efficient Language Model Fine-TuningDocumentationFor more information, check out thedocumentation.ContributingWe welcome contributions from the community! To contribute, please submit a pull request following our contributing guidelines.LicenseOpenGPT is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.
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