Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsArceeOn this pageArceeThis notebook demonstrates how to use the Arcee class for generating text using Arcee's Domain Adapted Language Models (DALMs).Setup‚ÄãBefore using Arcee, make sure the Arcee API key is set as ARCEE_API_KEY environment variable. You can also pass the api key as a named parameter.from langchain.llms import Arcee# Create an instance of the Arcee classarcee = Arcee(    model="DALM-PubMed",    # arcee_api_key="ARCEE-API-KEY" # if not already set in the environment)Additional Configuration‚ÄãYou can also configure Arcee's parameters such as arcee_api_url, arcee_app_url, and model_kwargs as needed.
This notebook demonstrates how to use the Arcee class for generating text using Arcee's Domain Adapted Language Models (DALMs).
This notebook demonstrates how to use the Arcee class for generating text using Arcee's Domain Adapted Language Models (DALMs). ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsArceeOn this pageArceeThis notebook demonstrates how to use the Arcee class for generating text using Arcee's Domain Adapted Language Models (DALMs).Setup‚ÄãBefore using Arcee, make sure the Arcee API key is set as ARCEE_API_KEY environment variable. You can also pass the api key as a named parameter.from langchain.llms import Arcee# Create an instance of the Arcee classarcee = Arcee(    model="DALM-PubMed",    # arcee_api_key="ARCEE-API-KEY" # if not already set in the environment)Additional Configuration‚ÄãYou can also configure Arcee's parameters such as arcee_api_url, arcee_app_url, and model_kwargs as needed.