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  • Welcome
  • INTRODUCTION
    • What Is Aizen?
    • Aizen Platform Interfaces
    • Typical ML Workflow
    • Datasets and Features
    • Resources and GPUs
    • LLM Operations
    • Glossary
  • INSTALLATION
    • Setting Up Your Environment
      • Hardware Requirements
      • Deploying Kubernetes On Prem
      • Deploying Kubernetes on AWS
      • Deploying Kubernetes on GCP
        • GCP and S3 API Interoperability
        • Provisioning the Cloud Service Mesh
        • Installing Ingress Gateways with Istio
      • Deploying Kubernetes on Azure
        • Setting Up Azure Blob Storage
    • Installing Aizen
      • Software Requirements
      • Installing the Infrastructure Components
      • Installing the Core Components
      • Virtual Services and Gateways Command Script (GCP)
      • Helpful Deployment Commands
    • Installing Aizen Remote Components
      • Static Remote Deployment
      • Dynamic Remote Deployment
    • Installing Optional Components
      • MinIO
      • OpenLDAP
      • OpenEBS Operator
      • NGINX Ingress Controller
      • Airbyte
  • GETTING STARTED
    • Managing Users and Roles
      • Aizen Security
      • Adding Users
      • Updating Users
      • Listing Users and Roles
      • Granting or Revoking Roles
      • Deleting Users
    • Accessing the Aizen Platform
    • Using the Aizen Jupyter Console
  • MANAGING ML WORKFLOWS
    • ML Workflow
    • Configuring Data Sources
    • Configuring Data Sinks
    • Creating Training Datasets
    • Performing ML Data Analysis
    • Training an ML Model
    • Adding Real-Time Data Sources
    • Serving an ML Model
    • Training and Serving Custom ML Models
  • MANAGING LLM WORKFLOWS
    • LLM Workflow
    • Configuring Data Sources
    • Creating Training Datasets for LLMs
    • Fine-Tuning an LLM
    • Serving an LLM
    • Adding Cloud Providers
    • Configuring Vector Stores
    • Running AI Agents
  • Notebook Commands Reference
    • Notebook Commands
  • SYSTEM CONFIGURATION COMMANDS
    • License Commands
      • check license
      • install license
    • Authorization Commands
      • add users
      • alter users
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      • grant role
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      • delete users
    • Cloud Provider Commands
      • add cloudprovider
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      • list filesystems
      • list instancetypes
      • status instance
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      • delete cloudprovider
    • Project Commands
      • create project
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      • exportconfig project
      • importconfig project
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      • listconfig all
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    • File Commands
      • install credentials
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  • MODEL BUILDING COMMANDS
    • Data Source Commands
      • configure datasource
      • describe datasource
      • listconfig datasources
      • delete datasource
    • Data Sink Commands
      • configure datasink
      • describe datasink
      • listconfig datasinks
      • alter datasink
      • start datasink
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      • delete datasink
    • Dataset Commands
      • configure dataset
      • describe dataset
      • listconfig datasets
      • exportconfig dataset
      • importconfig dataset
      • start dataset
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      • delete dataset
    • Data Analysis Commands
      • loader
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      • show unique
      • count rows
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      • plot
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      • run pca
      • filter dataframe
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      • set dataframe
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    • Training Commands
      • configure training
      • describe training
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      • start training
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      • delete training
      • list mlflow
      • save embedding
      • list trained-models
      • list trained-model
      • export trained-model
      • import trained-model
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      • register model
      • update model
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      • list registered-model
  • MODEL SERVING COMMANDS
    • Resource Commands
      • configure resource
      • describe resource
      • listconfig resources
      • alter resource
      • delete resource
    • Prediction Commands
      • configure prediction
      • describe prediction
      • listconfig predictions
      • start prediction
      • status prediction
      • test prediction
      • list predictions
      • stop prediction
      • list prediction-logs
      • display prediction-log
      • delete prediction
    • Data Report Commands
      • configure datareport
      • describe datareport
      • listconfig datareports
      • start datareport
      • list data-quality
      • list data-drift
      • list target-drift
      • status data-quality
      • display data-quality
      • status data-drift
      • display data-drift
      • status target-drift
      • display target-drift
      • delete datareport
    • Runtime Commands
      • configure runtime
      • describe runtime
      • listconfig runtimes
      • start runtime
      • status runtime
      • stop runtime
      • delete runtime
  • LLM AND EMBEDDINGS COMMANDS
    • LLM Commands
      • configure llm
      • listconfig llms
      • describe llm
      • start llm
      • status llm
      • stop llm
      • delete llm
    • Vector Store Commands
      • configure vectorstore
      • describe vectorstore
      • listconfig vectorstores
      • start vectorstore
      • status vectorstore
      • stop vectorstore
      • delete vectorstore
    • LLM Application Commands
      • configure llmapp
      • describe llmapp
      • listconfig llmapps
      • start llmapp
      • status llmapp
      • stop llmapp
      • delete llmapp
  • TROUBLESHOOTING
    • Installation Issues
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  • Syntax
  • Parameters
  • Example
  1. LLM AND EMBEDDINGS COMMANDS
  2. LLM Commands

configure llm

PreviousLLM CommandsNextlistconfig llms

Last updated 3 months ago

The configure llm command configures an LLM job. It is used to deploy an LLM or embeddings model for inference. In the notebook, you will be guided through a template form that will prompt you for various inputs, such as the LLM name, the source type, and the model name.

Syntax

configure llm

Parameters

To create an LLM job:

  1. Select New from the LLM drop-down list. Specify a name for the LLM job in the LLM Name field.

  2. Select the type, which is either llm or embeddings.

  3. Select either huggingface or aizen for the source type.

    • For aizen fine-tuned LLMs, select the registered Model Name and Version.

    • For huggingface pretrained LLMs, enter the Model Name and the Hugging Face credentials.

  4. Select Advanced Settings to specify quantization parameters.

  5. Click the Save Configuration button to save the LLM job.

Example