Oracle Cloud Infrastructure v2.18.0 published on Friday, Nov 22, 2024 by Pulumi
oci.GenerativeAi.getModels
Explore with Pulumi AI
This data source provides the list of Models in Oracle Cloud Infrastructure Generative AI service.
Lists the models in a specific compartment. Includes pretrained base models and fine-tuned custom models.
Example Usage
import * as pulumi from "@pulumi/pulumi";
import * as oci from "@pulumi/oci";
const testModels = oci.GenerativeAi.getModels({
compartmentId: compartmentId,
capabilities: modelCapability,
displayName: modelDisplayName,
id: modelId,
state: modelState,
vendor: modelVendor,
});
import pulumi
import pulumi_oci as oci
test_models = oci.GenerativeAi.get_models(compartment_id=compartment_id,
capabilities=model_capability,
display_name=model_display_name,
id=model_id,
state=model_state,
vendor=model_vendor)
package main
import (
"github.com/pulumi/pulumi-oci/sdk/v2/go/oci/GenerativeAi"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := GenerativeAi.GetModels(ctx, &generativeai.GetModelsArgs{
CompartmentId: compartmentId,
Capabilities: modelCapability,
DisplayName: pulumi.StringRef(modelDisplayName),
Id: pulumi.StringRef(modelId),
State: pulumi.StringRef(modelState),
Vendor: pulumi.StringRef(modelVendor),
}, nil)
if err != nil {
return err
}
return nil
})
}
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Oci = Pulumi.Oci;
return await Deployment.RunAsync(() =>
{
var testModels = Oci.GenerativeAi.GetModels.Invoke(new()
{
CompartmentId = compartmentId,
Capabilities = modelCapability,
DisplayName = modelDisplayName,
Id = modelId,
State = modelState,
Vendor = modelVendor,
});
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.oci.GenerativeAi.GenerativeAiFunctions;
import com.pulumi.oci.GenerativeAi.inputs.GetModelsArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
final var testModels = GenerativeAiFunctions.getModels(GetModelsArgs.builder()
.compartmentId(compartmentId)
.capabilities(modelCapability)
.displayName(modelDisplayName)
.id(modelId)
.state(modelState)
.vendor(modelVendor)
.build());
}
}
variables:
testModels:
fn::invoke:
Function: oci:GenerativeAi:getModels
Arguments:
compartmentId: ${compartmentId}
capabilities: ${modelCapability}
displayName: ${modelDisplayName}
id: ${modelId}
state: ${modelState}
vendor: ${modelVendor}
Using getModels
Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.
function getModels(args: GetModelsArgs, opts?: InvokeOptions): Promise<GetModelsResult>
function getModelsOutput(args: GetModelsOutputArgs, opts?: InvokeOptions): Output<GetModelsResult>
def get_models(capabilities: Optional[Sequence[str]] = None,
compartment_id: Optional[str] = None,
display_name: Optional[str] = None,
filters: Optional[Sequence[_generativeai.GetModelsFilter]] = None,
id: Optional[str] = None,
state: Optional[str] = None,
vendor: Optional[str] = None,
opts: Optional[InvokeOptions] = None) -> GetModelsResult
def get_models_output(capabilities: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
compartment_id: Optional[pulumi.Input[str]] = None,
display_name: Optional[pulumi.Input[str]] = None,
filters: Optional[pulumi.Input[Sequence[pulumi.Input[_generativeai.GetModelsFilterArgs]]]] = None,
id: Optional[pulumi.Input[str]] = None,
state: Optional[pulumi.Input[str]] = None,
vendor: Optional[pulumi.Input[str]] = None,
opts: Optional[InvokeOptions] = None) -> Output[GetModelsResult]
func GetModels(ctx *Context, args *GetModelsArgs, opts ...InvokeOption) (*GetModelsResult, error)
func GetModelsOutput(ctx *Context, args *GetModelsOutputArgs, opts ...InvokeOption) GetModelsResultOutput
> Note: This function is named GetModels
in the Go SDK.
public static class GetModels
{
public static Task<GetModelsResult> InvokeAsync(GetModelsArgs args, InvokeOptions? opts = null)
public static Output<GetModelsResult> Invoke(GetModelsInvokeArgs args, InvokeOptions? opts = null)
}
public static CompletableFuture<GetModelsResult> getModels(GetModelsArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet
fn::invoke:
function: oci:GenerativeAi/getModels:getModels
arguments:
# arguments dictionary
The following arguments are supported:
- Compartment
Id string - The OCID of the compartment in which to list resources.
- Capabilities List<string>
- A filter to return only resources their capability matches the given capability.
- Display
Name string - A filter to return only resources that match the given display name exactly.
- Filters
List<Get
Models Filter> - Id string
- The ID of the model.
- State string
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- Vendor string
- A filter to return only resources that match the entire vendor given.
- Compartment
Id string - The OCID of the compartment in which to list resources.
- Capabilities []string
- A filter to return only resources their capability matches the given capability.
- Display
Name string - A filter to return only resources that match the given display name exactly.
- Filters
[]Get
Models Filter - Id string
- The ID of the model.
- State string
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- Vendor string
- A filter to return only resources that match the entire vendor given.
- compartment
Id String - The OCID of the compartment in which to list resources.
- capabilities List<String>
- A filter to return only resources their capability matches the given capability.
- display
Name String - A filter to return only resources that match the given display name exactly.
- filters
List<Get
Models Filter> - id String
- The ID of the model.
- state String
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- vendor String
- A filter to return only resources that match the entire vendor given.
- compartment
Id string - The OCID of the compartment in which to list resources.
- capabilities string[]
- A filter to return only resources their capability matches the given capability.
- display
Name string - A filter to return only resources that match the given display name exactly.
- filters
Get
Models Filter[] - id string
- The ID of the model.
- state string
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- vendor string
- A filter to return only resources that match the entire vendor given.
- compartment_
id str - The OCID of the compartment in which to list resources.
- capabilities Sequence[str]
- A filter to return only resources their capability matches the given capability.
- display_
name str - A filter to return only resources that match the given display name exactly.
- filters
Sequence[generativeai.
Get Models Filter] - id str
- The ID of the model.
- state str
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- vendor str
- A filter to return only resources that match the entire vendor given.
- compartment
Id String - The OCID of the compartment in which to list resources.
- capabilities List<String>
- A filter to return only resources their capability matches the given capability.
- display
Name String - A filter to return only resources that match the given display name exactly.
- filters List<Property Map>
- id String
- The ID of the model.
- state String
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- vendor String
- A filter to return only resources that match the entire vendor given.
getModels Result
The following output properties are available:
- Compartment
Id string - The compartment OCID for fine-tuned models. For pretrained models, this value is null.
- Model
Collections List<GetModels Model Collection> - The list of model_collection.
- Capabilities List<string>
- Display
Name string - A user-friendly name.
- Filters
List<Get
Models Filter> - Id string
- An ID that uniquely identifies a pretrained or fine-tuned model.
- State string
- The lifecycle state of the model.
- Vendor string
- The provider of the base model.
- Compartment
Id string - The compartment OCID for fine-tuned models. For pretrained models, this value is null.
- Model
Collections []GetModels Model Collection - The list of model_collection.
- Capabilities []string
- Display
Name string - A user-friendly name.
- Filters
[]Get
Models Filter - Id string
- An ID that uniquely identifies a pretrained or fine-tuned model.
- State string
- The lifecycle state of the model.
- Vendor string
- The provider of the base model.
- compartment
Id String - The compartment OCID for fine-tuned models. For pretrained models, this value is null.
- model
Collections List<GetModels Model Collection> - The list of model_collection.
- capabilities List<String>
- display
Name String - A user-friendly name.
- filters
List<Get
Models Filter> - id String
- An ID that uniquely identifies a pretrained or fine-tuned model.
- state String
- The lifecycle state of the model.
- vendor String
- The provider of the base model.
- compartment
Id string - The compartment OCID for fine-tuned models. For pretrained models, this value is null.
- model
Collections GetModels Model Collection[] - The list of model_collection.
- capabilities string[]
- display
Name string - A user-friendly name.
- filters
Get
Models Filter[] - id string
- An ID that uniquely identifies a pretrained or fine-tuned model.
- state string
- The lifecycle state of the model.
- vendor string
- The provider of the base model.
- compartment_
id str - The compartment OCID for fine-tuned models. For pretrained models, this value is null.
- model_
collections Sequence[generativeai.Get Models Model Collection] - The list of model_collection.
- capabilities Sequence[str]
- display_
name str - A user-friendly name.
- filters
Sequence[generativeai.
Get Models Filter] - id str
- An ID that uniquely identifies a pretrained or fine-tuned model.
- state str
- The lifecycle state of the model.
- vendor str
- The provider of the base model.
- compartment
Id String - The compartment OCID for fine-tuned models. For pretrained models, this value is null.
- model
Collections List<Property Map> - The list of model_collection.
- capabilities List<String>
- display
Name String - A user-friendly name.
- filters List<Property Map>
- id String
- An ID that uniquely identifies a pretrained or fine-tuned model.
- state String
- The lifecycle state of the model.
- vendor String
- The provider of the base model.
Supporting Types
GetModelsFilter
GetModelsModelCollection
GetModelsModelCollectionItem
- Base
Model stringId - The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
- Capabilities List<string>
- Describes what this model can be used for.
- Compartment
Id string - The OCID of the compartment in which to list resources.
- Dictionary<string, string>
- Description string
- An optional description of the model.
- Display
Name string - A filter to return only resources that match the given display name exactly.
- Fine
Tune List<GetDetails Models Model Collection Item Fine Tune Detail> - Details about fine-tuning a custom model.
- Dictionary<string, string>
- Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example:
{"Department": "Finance"}
- Id string
- The ID of the model.
- Is
Long boolTerm Supported - Whether a model is supported long-term. Only applicable to base models.
- Lifecycle
Details string - A message describing the current state of the model in more detail that can provide actionable information.
- Model
Metrics List<GetModels Model Collection Item Model Metric> - Model metrics during the creation of a new model.
- State string
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- Dictionary<string, string>
- System tags for this resource. Each key is predefined and scoped to a namespace. Example:
{"orcl-cloud.free-tier-retained": "true"}
- Time
Created string - The date and time that the model was created in the format of an RFC3339 datetime string.
- Time
Deprecated string - Corresponds to the time when the custom model and its associated foundation model will be deprecated.
- Time
Updated string - The date and time that the model was updated in the format of an RFC3339 datetime string.
- Type string
- The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
- Vendor string
- A filter to return only resources that match the entire vendor given.
- Version string
- The version of the model.
- Base
Model stringId - The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
- Capabilities []string
- Describes what this model can be used for.
- Compartment
Id string - The OCID of the compartment in which to list resources.
- map[string]string
- Description string
- An optional description of the model.
- Display
Name string - A filter to return only resources that match the given display name exactly.
- Fine
Tune []GetDetails Models Model Collection Item Fine Tune Detail - Details about fine-tuning a custom model.
- map[string]string
- Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example:
{"Department": "Finance"}
- Id string
- The ID of the model.
- Is
Long boolTerm Supported - Whether a model is supported long-term. Only applicable to base models.
- Lifecycle
Details string - A message describing the current state of the model in more detail that can provide actionable information.
- Model
Metrics []GetModels Model Collection Item Model Metric - Model metrics during the creation of a new model.
- State string
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- map[string]string
- System tags for this resource. Each key is predefined and scoped to a namespace. Example:
{"orcl-cloud.free-tier-retained": "true"}
- Time
Created string - The date and time that the model was created in the format of an RFC3339 datetime string.
- Time
Deprecated string - Corresponds to the time when the custom model and its associated foundation model will be deprecated.
- Time
Updated string - The date and time that the model was updated in the format of an RFC3339 datetime string.
- Type string
- The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
- Vendor string
- A filter to return only resources that match the entire vendor given.
- Version string
- The version of the model.
- base
Model StringId - The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
- capabilities List<String>
- Describes what this model can be used for.
- compartment
Id String - The OCID of the compartment in which to list resources.
- Map<String,String>
- description String
- An optional description of the model.
- display
Name String - A filter to return only resources that match the given display name exactly.
- fine
Tune List<GetDetails Models Model Collection Item Fine Tune Detail> - Details about fine-tuning a custom model.
- Map<String,String>
- Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example:
{"Department": "Finance"}
- id String
- The ID of the model.
- is
Long BooleanTerm Supported - Whether a model is supported long-term. Only applicable to base models.
- lifecycle
Details String - A message describing the current state of the model in more detail that can provide actionable information.
- model
Metrics List<GetModels Model Collection Item Model Metric> - Model metrics during the creation of a new model.
- state String
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- Map<String,String>
- System tags for this resource. Each key is predefined and scoped to a namespace. Example:
{"orcl-cloud.free-tier-retained": "true"}
- time
Created String - The date and time that the model was created in the format of an RFC3339 datetime string.
- time
Deprecated String - Corresponds to the time when the custom model and its associated foundation model will be deprecated.
- time
Updated String - The date and time that the model was updated in the format of an RFC3339 datetime string.
- type String
- The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
- vendor String
- A filter to return only resources that match the entire vendor given.
- version String
- The version of the model.
- base
Model stringId - The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
- capabilities string[]
- Describes what this model can be used for.
- compartment
Id string - The OCID of the compartment in which to list resources.
- {[key: string]: string}
- description string
- An optional description of the model.
- display
Name string - A filter to return only resources that match the given display name exactly.
- fine
Tune GetDetails Models Model Collection Item Fine Tune Detail[] - Details about fine-tuning a custom model.
- {[key: string]: string}
- Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example:
{"Department": "Finance"}
- id string
- The ID of the model.
- is
Long booleanTerm Supported - Whether a model is supported long-term. Only applicable to base models.
- lifecycle
Details string - A message describing the current state of the model in more detail that can provide actionable information.
- model
Metrics GetModels Model Collection Item Model Metric[] - Model metrics during the creation of a new model.
- state string
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- {[key: string]: string}
- System tags for this resource. Each key is predefined and scoped to a namespace. Example:
{"orcl-cloud.free-tier-retained": "true"}
- time
Created string - The date and time that the model was created in the format of an RFC3339 datetime string.
- time
Deprecated string - Corresponds to the time when the custom model and its associated foundation model will be deprecated.
- time
Updated string - The date and time that the model was updated in the format of an RFC3339 datetime string.
- type string
- The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
- vendor string
- A filter to return only resources that match the entire vendor given.
- version string
- The version of the model.
- base_
model_ strid - The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
- capabilities Sequence[str]
- Describes what this model can be used for.
- compartment_
id str - The OCID of the compartment in which to list resources.
- Mapping[str, str]
- description str
- An optional description of the model.
- display_
name str - A filter to return only resources that match the given display name exactly.
- fine_
tune_ Sequence[generativeai.details Get Models Model Collection Item Fine Tune Detail] - Details about fine-tuning a custom model.
- Mapping[str, str]
- Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example:
{"Department": "Finance"}
- id str
- The ID of the model.
- is_
long_ boolterm_ supported - Whether a model is supported long-term. Only applicable to base models.
- lifecycle_
details str - A message describing the current state of the model in more detail that can provide actionable information.
- model_
metrics Sequence[generativeai.Get Models Model Collection Item Model Metric] - Model metrics during the creation of a new model.
- state str
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- Mapping[str, str]
- System tags for this resource. Each key is predefined and scoped to a namespace. Example:
{"orcl-cloud.free-tier-retained": "true"}
- time_
created str - The date and time that the model was created in the format of an RFC3339 datetime string.
- time_
deprecated str - Corresponds to the time when the custom model and its associated foundation model will be deprecated.
- time_
updated str - The date and time that the model was updated in the format of an RFC3339 datetime string.
- type str
- The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
- vendor str
- A filter to return only resources that match the entire vendor given.
- version str
- The version of the model.
- base
Model StringId - The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
- capabilities List<String>
- Describes what this model can be used for.
- compartment
Id String - The OCID of the compartment in which to list resources.
- Map<String>
- description String
- An optional description of the model.
- display
Name String - A filter to return only resources that match the given display name exactly.
- fine
Tune List<Property Map>Details - Details about fine-tuning a custom model.
- Map<String>
- Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example:
{"Department": "Finance"}
- id String
- The ID of the model.
- is
Long BooleanTerm Supported - Whether a model is supported long-term. Only applicable to base models.
- lifecycle
Details String - A message describing the current state of the model in more detail that can provide actionable information.
- model
Metrics List<Property Map> - Model metrics during the creation of a new model.
- state String
- A filter to return only resources their lifecycleState matches the given lifecycleState.
- Map<String>
- System tags for this resource. Each key is predefined and scoped to a namespace. Example:
{"orcl-cloud.free-tier-retained": "true"}
- time
Created String - The date and time that the model was created in the format of an RFC3339 datetime string.
- time
Deprecated String - Corresponds to the time when the custom model and its associated foundation model will be deprecated.
- time
Updated String - The date and time that the model was updated in the format of an RFC3339 datetime string.
- type String
- The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
- vendor String
- A filter to return only resources that match the entire vendor given.
- version String
- The version of the model.
GetModelsModelCollectionItemFineTuneDetail
- Dedicated
Ai stringCluster Id - The OCID of the dedicated AI cluster this fine-tuning runs on.
- Training
Configs List<GetModels Model Collection Item Fine Tune Detail Training Config> - The fine-tuning method and hyperparameters used for fine-tuning a custom model.
- Training
Datasets List<GetModels Model Collection Item Fine Tune Detail Training Dataset> - The dataset used to fine-tune the model.
- Dedicated
Ai stringCluster Id - The OCID of the dedicated AI cluster this fine-tuning runs on.
- Training
Configs []GetModels Model Collection Item Fine Tune Detail Training Config - The fine-tuning method and hyperparameters used for fine-tuning a custom model.
- Training
Datasets []GetModels Model Collection Item Fine Tune Detail Training Dataset - The dataset used to fine-tune the model.
- dedicated
Ai StringCluster Id - The OCID of the dedicated AI cluster this fine-tuning runs on.
- training
Configs List<GetModels Model Collection Item Fine Tune Detail Training Config> - The fine-tuning method and hyperparameters used for fine-tuning a custom model.
- training
Datasets List<GetModels Model Collection Item Fine Tune Detail Training Dataset> - The dataset used to fine-tune the model.
- dedicated
Ai stringCluster Id - The OCID of the dedicated AI cluster this fine-tuning runs on.
- training
Configs GetModels Model Collection Item Fine Tune Detail Training Config[] - The fine-tuning method and hyperparameters used for fine-tuning a custom model.
- training
Datasets GetModels Model Collection Item Fine Tune Detail Training Dataset[] - The dataset used to fine-tune the model.
- dedicated_
ai_ strcluster_ id - The OCID of the dedicated AI cluster this fine-tuning runs on.
- training_
configs Sequence[generativeai.Get Models Model Collection Item Fine Tune Detail Training Config] - The fine-tuning method and hyperparameters used for fine-tuning a custom model.
- training_
datasets Sequence[generativeai.Get Models Model Collection Item Fine Tune Detail Training Dataset] - The dataset used to fine-tune the model.
- dedicated
Ai StringCluster Id - The OCID of the dedicated AI cluster this fine-tuning runs on.
- training
Configs List<Property Map> - The fine-tuning method and hyperparameters used for fine-tuning a custom model.
- training
Datasets List<Property Map> - The dataset used to fine-tune the model.
GetModelsModelCollectionItemFineTuneDetailTrainingConfig
- Early
Stopping intPatience - Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
- Early
Stopping doubleThreshold - How much the loss must improve to prevent early stopping.
- Learning
Rate double - The initial learning rate to be used during training
- Log
Model intMetrics Interval In Steps - Determines how frequently to log model metrics.
- Lora
Alpha int - This parameter represents the scaling factor for the weight matrices in LoRA.
- Lora
Dropout double - This parameter indicates the dropout probability for LoRA layers.
- Lora
R int - This parameter represents the LoRA rank of the update matrices.
- Num
Of intLast Layers - The number of last layers to be fine-tuned.
- Total
Training intEpochs - The maximum number of training epochs to run for.
- Training
Batch intSize - The batch size used during training.
- Training
Config stringType - The fine-tuning method for training a custom model.
- Early
Stopping intPatience - Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
- Early
Stopping float64Threshold - How much the loss must improve to prevent early stopping.
- Learning
Rate float64 - The initial learning rate to be used during training
- Log
Model intMetrics Interval In Steps - Determines how frequently to log model metrics.
- Lora
Alpha int - This parameter represents the scaling factor for the weight matrices in LoRA.
- Lora
Dropout float64 - This parameter indicates the dropout probability for LoRA layers.
- Lora
R int - This parameter represents the LoRA rank of the update matrices.
- Num
Of intLast Layers - The number of last layers to be fine-tuned.
- Total
Training intEpochs - The maximum number of training epochs to run for.
- Training
Batch intSize - The batch size used during training.
- Training
Config stringType - The fine-tuning method for training a custom model.
- early
Stopping IntegerPatience - Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
- early
Stopping DoubleThreshold - How much the loss must improve to prevent early stopping.
- learning
Rate Double - The initial learning rate to be used during training
- log
Model IntegerMetrics Interval In Steps - Determines how frequently to log model metrics.
- lora
Alpha Integer - This parameter represents the scaling factor for the weight matrices in LoRA.
- lora
Dropout Double - This parameter indicates the dropout probability for LoRA layers.
- lora
R Integer - This parameter represents the LoRA rank of the update matrices.
- num
Of IntegerLast Layers - The number of last layers to be fine-tuned.
- total
Training IntegerEpochs - The maximum number of training epochs to run for.
- training
Batch IntegerSize - The batch size used during training.
- training
Config StringType - The fine-tuning method for training a custom model.
- early
Stopping numberPatience - Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
- early
Stopping numberThreshold - How much the loss must improve to prevent early stopping.
- learning
Rate number - The initial learning rate to be used during training
- log
Model numberMetrics Interval In Steps - Determines how frequently to log model metrics.
- lora
Alpha number - This parameter represents the scaling factor for the weight matrices in LoRA.
- lora
Dropout number - This parameter indicates the dropout probability for LoRA layers.
- lora
R number - This parameter represents the LoRA rank of the update matrices.
- num
Of numberLast Layers - The number of last layers to be fine-tuned.
- total
Training numberEpochs - The maximum number of training epochs to run for.
- training
Batch numberSize - The batch size used during training.
- training
Config stringType - The fine-tuning method for training a custom model.
- early_
stopping_ intpatience - Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
- early_
stopping_ floatthreshold - How much the loss must improve to prevent early stopping.
- learning_
rate float - The initial learning rate to be used during training
- log_
model_ intmetrics_ interval_ in_ steps - Determines how frequently to log model metrics.
- lora_
alpha int - This parameter represents the scaling factor for the weight matrices in LoRA.
- lora_
dropout float - This parameter indicates the dropout probability for LoRA layers.
- lora_
r int - This parameter represents the LoRA rank of the update matrices.
- num_
of_ intlast_ layers - The number of last layers to be fine-tuned.
- total_
training_ intepochs - The maximum number of training epochs to run for.
- training_
batch_ intsize - The batch size used during training.
- training_
config_ strtype - The fine-tuning method for training a custom model.
- early
Stopping NumberPatience - Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
- early
Stopping NumberThreshold - How much the loss must improve to prevent early stopping.
- learning
Rate Number - The initial learning rate to be used during training
- log
Model NumberMetrics Interval In Steps - Determines how frequently to log model metrics.
- lora
Alpha Number - This parameter represents the scaling factor for the weight matrices in LoRA.
- lora
Dropout Number - This parameter indicates the dropout probability for LoRA layers.
- lora
R Number - This parameter represents the LoRA rank of the update matrices.
- num
Of NumberLast Layers - The number of last layers to be fine-tuned.
- total
Training NumberEpochs - The maximum number of training epochs to run for.
- training
Batch NumberSize - The batch size used during training.
- training
Config StringType - The fine-tuning method for training a custom model.
GetModelsModelCollectionItemFineTuneDetailTrainingDataset
- Bucket string
- The Object Storage bucket name.
- Dataset
Type string - The type of the data asset.
- Namespace string
- The Object Storage namespace.
- Object string
- The Object Storage object name.
- Bucket string
- The Object Storage bucket name.
- Dataset
Type string - The type of the data asset.
- Namespace string
- The Object Storage namespace.
- Object string
- The Object Storage object name.
- bucket String
- The Object Storage bucket name.
- dataset
Type String - The type of the data asset.
- namespace String
- The Object Storage namespace.
- object String
- The Object Storage object name.
- bucket string
- The Object Storage bucket name.
- dataset
Type string - The type of the data asset.
- namespace string
- The Object Storage namespace.
- object string
- The Object Storage object name.
- bucket str
- The Object Storage bucket name.
- dataset_
type str - The type of the data asset.
- namespace str
- The Object Storage namespace.
- object str
- The Object Storage object name.
- bucket String
- The Object Storage bucket name.
- dataset
Type String - The type of the data asset.
- namespace String
- The Object Storage namespace.
- object String
- The Object Storage object name.
GetModelsModelCollectionItemModelMetric
- Final
Accuracy double - Fine-tuned model accuracy.
- Final
Loss double - Fine-tuned model loss.
- Model
Metrics stringType - The type of the model metrics. Each type of model can expect a different set of model metrics.
- Final
Accuracy float64 - Fine-tuned model accuracy.
- Final
Loss float64 - Fine-tuned model loss.
- Model
Metrics stringType - The type of the model metrics. Each type of model can expect a different set of model metrics.
- final
Accuracy Double - Fine-tuned model accuracy.
- final
Loss Double - Fine-tuned model loss.
- model
Metrics StringType - The type of the model metrics. Each type of model can expect a different set of model metrics.
- final
Accuracy number - Fine-tuned model accuracy.
- final
Loss number - Fine-tuned model loss.
- model
Metrics stringType - The type of the model metrics. Each type of model can expect a different set of model metrics.
- final_
accuracy float - Fine-tuned model accuracy.
- final_
loss float - Fine-tuned model loss.
- model_
metrics_ strtype - The type of the model metrics. Each type of model can expect a different set of model metrics.
- final
Accuracy Number - Fine-tuned model accuracy.
- final
Loss Number - Fine-tuned model loss.
- model
Metrics StringType - The type of the model metrics. Each type of model can expect a different set of model metrics.
Package Details
- Repository
- oci pulumi/pulumi-oci
- License
- Apache-2.0
- Notes
- This Pulumi package is based on the
oci
Terraform Provider.