Model Limitations in Generative AI

Review the following model requirements for the OCI Generative AI custom and base models to get the most out of your models.

Note

For key features of the pretrained base models, see Pretrained Foundational Models in Generative AI.

Matching Base Models to Clusters

Expand the following sections to review the dedicated AI cluster unit size and units that match each foundational model.

Chat

Important

Some OCI Generative AI foundational pretrained models supported for the dedicated serving mode are now deprecated and will retire no sooner than 6 months after the release of the 1^st replacement model. You can fine-tune and host a foundational pretrained model on a dedicated AI cluster (dedicated serving mode) until that model is retired. For dedicated serving mode retirement dates, see Retiring the Models.


Base Model	Fine-Tuning Cluster	Hosting Cluster	Pricing Page Information	Request Cluster Limit Increase
Model Name: Cohere Command R OCI Model Name: `cohere.command-r-16k`	Unit Size: Small Cohere V2 Required Units: 8	Unit Size: Small Cohere V2 Required Units: 1	Pricing Page Product Name: Small Cohere - Dedicated For Fine-Tuning, Multiply the Unit Price: x8	Limit Name: `dedicated-unit-small-cohere-count` For Hosting, Request Limit Increase by: 1 For Fine-Tuning, Request Limit Increase by: 8
Model Name: Cohere Command R+ OCI Model Name: `cohere.command-r-plus`	Not available for fine-tuning	Unit Size: Large Cohere V2_2 Required Units: 1	Pricing Page Product Name: Large Cohere - Dedicated For Hosting, Multiply the Unit Price: x2	Limit Name: `dedicated-unit-large-cohere-count` For Hosting, Request Limit Increase by: 2
Model Name: Meta Llama 3.1 (70B) OCI Model Name: `meta.llama-3.1-70b-instruct`	Unit Size: Large Generic Required Units: 2	Unit Size: Large Generic Required Units: 1	Pricing Page Product Name: Large Meta - Dedicated For Hosting, Multiply the Unit Price: x2 For Fine-Tuning, Multiply the Unit Price: x4	Limit Name: `dedicated-unit-llama2-70-count` For Hosting, Request Limit Increase by: 2 For Fine-Tuning, Request Limit Increase by: 4
Model Name: Meta Llama 3.1 (405B) OCI Model Name: `meta.llama-3.1-405b-instruct`	Not available for fine-tuning	Unit Size: Large Generic 4 Required Units: 1	Pricing Page Product Name: Large Meta - Dedicated For Hosting, Multiply the Unit Price: x8	Limit Name: `dedicated-unit-llama2-70-count` For Hosting, Request Limit Increase by: 8
Model Name: Meta Llama 3 OCI Model Name: `meta.llama-3-70b-instruct` (deprecated)	Unit Size: Large Generic Required Units: 2	Unit Size: Large Generic Required Units: 1	Pricing Page Product Name: Large Meta - Dedicated For Hosting, Multiply the Unit Price: x2 For Fine-Tuning, Multiply the Unit Price: x4	Limit Name: `dedicated-unit-llama2-70-count` For Hosting, Request Limit Increase by: 2 For Fine-Tuning, Request Limit Increase by: 4

Important

You must request a limit increase to use the following resources:

Meta Llama Family

To host a Meta Llama 3.1 (405B) model, you must request dedicated-unit-llama2-70-count to increase by 8.
To host a Meta Llama 3.1 (70B) model, you must request dedicated-unit-llama2-70-count to increase by 2.
To fine-tune a Meta Llama 3.1 (70B) model, you must request dedicated-unit-llama2-70-count to increase by 4.

Cohere Command R Family

To host a Cohere Command R+ model, you must request dedicated-unit-large-cohere-count to increase by 2.
To host a Cohere Command R model, you must request dedicated-unit-small-cohere-count to increase by 1.
To fine-tune a Cohere Command R model, you must request dedicated-unit-small-cohere-count to increase by 8.

References: Service Limits for Generative AI and Request Cluster Limit Increase

Embedding


Base Model	Fine-tuning Cluster	Hosting Cluster	Pricing Page Product Name	Request Cluster Limit Increase
Model Name: Cohere English Embed V3 OCI Model Name: `cohere.embed-english-v3.0`	Not available for fine-tuning	Unit Size: Embed Cohere Required Units: 1	Pricing Page Product Name: Embed Cohere - Dedicated For Hosting, Multiply the Unit Price: x1	Limit Name: `dedicated-unit-embed-cohere-count` For Hosting, Request Limit Increase by: 1
Model Name: Cohere Multilingual Embed V3 OCI Model Name: `cohere.embed-multilingual-v3.0`	Not available for fine-tuning	Unit Size: Embed Cohere Required Units: 1	Pricing Page Product Name: Embed Cohere - Dedicated For Hosting, Multiply the Unit Price: x1	Limit Name: `dedicated-unit-embed-cohere-count` For Hosting, Request Limit Increase by: 1
Model Name: Cohere English Light Embed V3 OCI Model Name: `cohere.embed-english-light-v3.0`	Not available for fine-tuning	Unit Size: Embed Cohere Required Units: 1	Pricing Page Product Name: Embed Cohere - Dedicated For Hosting, Multiply the Unit Price: x1	Limit Name: `dedicated-unit-embed-cohere-count` For Hosting, Request Limit Increase by: 1
Model Name: Cohere Multilingual Light Embed V3 OCI Model Name: `cohere.embed-multilingual-light-v3.0`	Not available for fine-tuning	Unit Size: Embed Cohere Required Units: 1	Pricing Page Product Name: Embed Cohere - Dedicated For Hosting, Multiply the Unit Price: x1	Limit Name: `dedicated-unit-embed-cohere-count` For Hosting, Request Limit Increase by: 1

Text Generation (Deprecated)

Important

All OCI Generative AI foundational pretrained models supported for the on-demand serving mode that use the text generation and summarization APIs (including the playground) are now retired. If you host a summarization or a generation model such as cohere.command on a dedicated AI cluster, (dedicated serving mode), you can continue to use that model until it's retired. See Retiring the Models for retirement dates and definitions. We recommend that you use the chat models instead.


Base Model	Fine-tuning Cluster	Hosting Cluster	Pricing Page Product Name	Request Cluster Limit Increase
Model Name: Cohere Command XL (52B) OCI Model Name: `cohere.command` (deprecated)	Unit Size: Large Cohere Required Units: 2	Unit Size: Large Cohere Required Units: 1	Pricing Page Product Name: Large Cohere - Dedicated For Hosting, Multiply the Unit Price: x1 For Fine-Tuning, Multiply the Unit Price: x2	Limit Name: `dedicated-unit-large-cohere-count` For Hosting, Request Limit Increase by: 1 For Fine-Tuning, Request Limit Increase by: 2
Model Name: Cohere Command Light (6B) OCI Model Name: `cohere.command-light` (deprecated)	Unit Size: Small Cohere Required Units: 2	Unit Size: Small Cohere Required Units: 1	Pricing Page Product Name: Small Cohere - Dedicated For Hosting, Multiply the Unit Price: x1 For Fine-Tuning, Multiply the Unit Price: x2	Limit Name: `dedicated-unit-small-cohere-count` For Hosting, Request Limit Increase by: 1 For Fine-Tuning, Request Limit Increase by: 2
Model Name: Meta Llama 2 OCI Model Name: `meta.llama-2-70b-chat` (deprecated)	Not available for fine-tuning	Unit Size: Llama2 70 Required Units: 1	Pricing Page Product Name: Large Meta - Dedicated For Hosting, Multiply the Unit Price: x1	Limit Name: `dedicated-unit-llama2-70-count` For Hosting, Request Limit Increase by: 1

Summarization (Deprecated)

Important

The cohere.command model supported for the on-demand serving mode is now retired and this model is deprecated for the dedicated serving mode. If you're hosting cohere.command on a dedicated AI cluster, (dedicated serving mode) for summarization, you can continue to use this hosted model replica with the summarization API and in the playground until the cohere.command model retires for the dedicated serving mode. See Retiring the Models for retirement dates and definitions. We recommend that you use the chat models instead which offer the same summarization capabilities, including control over summary length and style.


Base Model	Fine-tuning Cluster	Hosting Cluster	Pricing Page Product Name	Request Cluster Limit Increase
Model Name: Cohere Command XL (52B) OCI Model Name: `cohere.command` (deprecated)	Not available for fine-tuning	Unit Size: Large Cohere Required Units: 1	Pricing Page Product Name: Large Cohere - Dedicated For Hosting, Multiply the Unit Price: x1	Limit Name: `dedicated-unit-large-cohere-count` For Hosting, Request Limit Increase by: 1

Units for Fine-Tuning Clusters

Creating a fine-tuning dedicated AI cluster automatically provisions a fixed number of units based on the base model: 8 units for cohere.command-r-16k and 2 units for other models. You can't change this number, but you can use the same cluster to fine-tune several models.

Units for Hosting Clusters

When creating a cluster, by default, one unit is created for the selected base model.
You can increase throughput or requests per minute (RPM) by adding model replicas. For example, 2 replicas require 2 units. You can add model replicas when creating or editing a hosting cluster.
Host up to 50 models on the same cluster, with the following restrictions:
- Host up to 50 of the same version of a fine-tuned or a pretrained model on the same cluster.
- Host different versions of the same base model, only if using T-FEW fine-tuning method for cohere.command and cohere.command-light base models.

Note

Instead of committing to dedicated AI clusters, you can pay as you go for on-demand inferencing. With on-demand inferencing you reach the foundational models either through the Console, in the playground or through the API. For on-demand features, see Calculating Cost in Generative AI.

Adding Endpoints to Hosting Clusters

To host a model for inference on a hosting dedicated AI cluster, you must create an endpoint for that model. Then, you can add either add a custom model or a pretrained foundational model to that endpoint.

About Endpoint Aliases and Stack Serving

A hosting dedicated AI cluster can have up to 50 endpoints. Use these endpoints for the following use cases:

Creating Endpoint Aliases: Create aliases with many endpoints. These 50 endpoints must either point to the same base model or the same version of a custom model. Creating many endpoints that point to the same model makes it easier to manage the endpoints, because you can use the endpoints for different users or different purposes.
Stack Serving: Host several versions of a custom model on one cluster. This applies to cohere.command and cohere.command-light models that are fine-tuned with the T-Few training method. Hosting various versions of a fine-tuned model can help you to assess the custom models for different use cases.

Tip

To increase the call volume supported by a hosting cluster, you can increase its instance count.

Expand the following sections to review the requirements for hosting models on the same cluster.

Chat

For hosting the pretrained or fine-tuned chat models on a hosting dedicated AI cluster, use the following cluster unit size and endpoint rules that match each base model.


Hosting Cluster Unit Size	Matching Rules
Large Generic	To host the same pretrained base model through several endpoints on the same cluster: Create as many endpoints as needed for the `meta.llama-3-70b-instruct` model on the same hosting cluster. To host several custom models on the same cluster: Fine-tune one model with the `LoRA` training method. Use the `meta.llama-3-70b-instruct` model as the base. Create as many endpoints as needed for the custom model (same version).
Large Generic	To host the same pretrained base model through several endpoints on the same cluster: Create as many endpoints as needed for the `meta.llama-3.1-70b-instruct` model on the same hosting cluster. To host several custom models on the same cluster: Fine-tune one model with the `LoRA` training method. Use the `meta.llama-3.1-70b-instruct` model as the base. Create as many endpoints as needed for the custom model (same version).
Large Generic 4	To host the same pretrained base model through several endpoints on the same cluster: Create as many endpoints as needed for the `meta.llama-3.1-405b-instruct` model on the same hosting cluster.
Large Cohere V2_2	To host the same pretrained base model through several endpoints on the same cluster: Create as many endpoints as needed for the `cohere.command-r-plus` model on the same hosting cluster.
Small Cohere V2	To host the same pretrained base model through several endpoints on the same cluster: Create as many endpoints as needed for the `cohere.command-r-16k` model on the same hosting cluster. To host several custom models on the same cluster: Fine-tune one model with the `T-Few` training method. Use the `cohere.command-r-16k` model as the base. Create as many endpoints as needed for the custom model (same version). You can't host different versions of a custom model trained on the `cohere.command-r-16k` base model on the same cluster, as stack serving isn't supported.

Embedding

For hosting the embedding models on a hosting dedicated AI cluster, use the following cluster unit size and endpoint rules.


Hosting Cluster Unit Size	Matching Rules
Embed Cohere	To host the same pretrained base model through several endpoints on the same cluster: Create as many endpoints as needed for the `cohere.embed-<models>` model with the exact same model name and version on the same hosting cluster.

Text Generation (Deprecated)

To host the text generation models on a hosting dedicated AI cluster, use the following cluster unit size and endpoint rules that match your base model.


Hosting Cluster Unit Size	Matching Rules
Small Cohere	To host the same pretrained base model through several endpoints on the same cluster: Create as many endpoints as needed for the `cohere.command-light` model on the same hosting cluster. To host different custom models on the same cluster: Fine-tune all the models with the `T-Few` training method. Use the `cohere.command-light` model as the base. Ensure that all base models have the same version. Create an endpoint for each model on the same hosting cluster.
Large Cohere	To host the same pretrained base model through several endpoints on the same cluster: Create as many endpoints as needed for the `cohere.command` model with the same version on the same hosting cluster. To host different custom models on the same cluster: Fine-tune all the models with the `T-Few` training method. Use the `cohere.command` model as the base. Ensure that all base models have the same version. Add an endpoint to the hosting cluster for each model.
Llama2 70	To host the same pretrained base model through several endpoints on the same cluster: Create as many endpoints as needed for the `meta.llama-2-70b-chat` model on the same hosting cluster.

Summarization (Deprecated)

To host the pretrained cohere.command summarization model on a hosting dedicated AI cluster, use the following cluster unit size and endpoint rules.


Hosting Cluster Unit Size	Matching Rules
Large Cohere	To host the same pretrained base model through several endpoints on the same cluster: Create as many endpoints as needed for the `cohere.command` model with the same version on the same hosting cluster. To host different custom models on the same cluster: Fine-tune all the models with the `T-Few` training method. Use the `cohere.command` model as the base. Ensure that all base models have the same version. Add an endpoint to the hosting cluster for each model.

Training Data

Datasets for training custom models have the following requirements:

A maximum of one fine-tuning dataset is allowed per custom model. This dataset is randomly split to a 80:20 ratio for training and validating.
Each file must have at least 32 prompt/completion pair examples.
The file format is JSONL.
Each line in the JSONL file has the following format:
{"prompt": "<a prompt>", "completion": "<expected response given the prompt>"}\n
The file must be stored in an OCI Object Storage bucket.

Learn about Training Data Requirements in Generative AI.

Input Data for Text Embeddings

Input data for creating text embeddings has the following requirements:

You can add sentences, phrases, or paragraphs for embeddings either one phrase at a time, or by uploading a file.
Only files with a .txt extension are allowed.
If you use an input file, each input sentence, phrase, or paragraph in the file must be separated with a newline character.
A maximum of 96 inputs are allowed for each run.
Each input must be less than 512 tokens. If an input is too long, select whether to cut off the start or the end of the text to fit within the token limit by setting the Truncate parameter to Start or End. If an input exceeds the 512 token limit and the Truncate parameter is set to None, you get an error message.

Learn about Creating text embeddings in OCI Generative AI.