text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAleph AlphaAzureOpenAIBedrock EmbeddingsClarifaiCohereDashScopeDeepInfraElasticsearchEmbaasFake EmbeddingsGoogle Cloud Platform Vertex AI PaLMGPT4AllHugging Face HubInstructEmbeddingsJinaLlama-cppLocalAIMiniMaxModelScopeMosaicML embeddingsNLP CloudOpenAISageMaker Endpoint EmbeddingsSelf Hosted EmbeddingsSentence Transformers EmbeddingsSpacy EmbeddingTensorflowHubAgent toolkitsToolsVector storesGrouped by providerIntegrationsText embedding modelsSelf Hosted EmbeddingsSelf Hosted EmbeddingsLet's load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes.from langchain.embeddings import (    SelfHostedEmbeddings,    SelfHostedHuggingFaceEmbeddings,    SelfHostedHuggingFaceInstructEmbeddings,)import runhouse as rh# For an on-demand A100 with GCP, Azure, or Lambdagpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)# For an on-demand A10G with AWS (no single A100s on AWS)# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')# For an existing cluster# gpu = rh.cluster(ips=['<ip of the cluster>'],#                  ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},#