Oracle Cloud Infrastructure Documentation

Cohere Embed Multilingual Light 3

The cohere.embed-multilingual-light-v3.0 model transforms each phrase, sentence, or paragraph that you input, into an array.

You can use the embedding models for finding similarity in phrases that are similar in context or category. Embeddings are typically stored in a vector database. Embeddings are mostly used for semantic searches where the search function focuses on the meaning of the text that it's searching through rather than finding results based on keywords.

Available in This Region

  • US Midwest (Chicago)

Key Features

  • Light models are smaller and faster than the original models.
  • English or multilingual.
  • Model creates a 384-dimensional vector for each embedding.
  • Maximum 96 sentences per run.
  • Maximum 512 tokens for each input.
  • Best for use cases when:

On-Demand Mode

This model is available on-demand in regions not listed as (dedicated AI cluster only). See the following table for this model's on-demand product name on the pricing page.

Model Name OCI Model Name Pricing Page Product Name
Cohere Embed Multilingual Light 3 cohere.embed-multilingual-light-v3.0 Embed Cohere
You can reach the pretrained foundational models in Generative AI through two modes: on-demand and dedicated. Here are key features for the on-demand mode:

  • You pay as you go for each inference call when you use the models in the playground or when you call the models through the API.

  • Low barrier to start using Generative AI.
  • Great for experimentation, proof of concept, and model evaluation.
  • Available for the pretrained models in regions not listed as (dedicated AI cluster only).
Important

Dynamic Throttling Limit Adjustment for On-Demand Mode

OCI Generative AI dynamically adjusts the request throttling limit for each active tenancy based on model demand and system capacity to optimize resource allocation and ensure fair access.

This adjustment depends on the following factors:

  • The current maximum throughput supported by the target model.
  • Any unused system capacity at the time of adjustment.
  • Each tenancy’s historical throughput usage and any specified override limits set for that tenancy.

Note: Because of dynamic throttling, rate limits are undocumented and can change to meet system-wide demand.

Tip

Because of the dynamic throttling limit adjustment, we recommend implementing a back-off strategy, which involves delaying requests after a rejection. Without one, repeated rapid requests can lead to further rejections over time, increased latency, and potential temporary blocking of client by the Generative AI service. By using a back-off strategy, such as an exponential back-off strategy, you can distribute requests more evenly, reduce load, and improve retry success, following industry best practices and enhancing the overall stability and performance of your integration to the service.

Dedicated AI Cluster for the Model

To reach a model through a dedicated AI cluster in any listed region, you must create an endpoint for that model on a dedicated AI cluster. For the cluster unit size that matches this model, see the following table.

Base Model Fine-Tuning Cluster Hosting Cluster Pricing Page Information Request Cluster Limit Increase
  • Model Name: Cohere Embed Multilingual Light 3
  • 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
Tip

  • The Cohere Embed Multilingual Light 3 model has both on-demand and dedicated AI cluster options. For on-demand mode, you don't need clusters and you can reach the model in the Console playground or through the API.

  • If you don't have enough cluster limits in your tenancy for hosting an Embed model on a dedicated AI cluster, request the dedicated-unit-embed-cohere-count limit to increase by 1.

Endpoint Rules for Clusters

  • A dedicated AI cluster can hold up to 50 endpoints.
  • Use these endpoints to create aliases that all point either to the same base model or to the same version of a custom model, but not both types.
  • Several endpoints for the same model make it easy to assign them to different users or purposes.
Hosting Cluster Unit Size Endpoint Rules
Embed Cohere
  • Base model: To run the cohere.embed-multilingual-light-v3.0 model on several endpoints, create as many endpoints as you need on a Embed Cohere cluster (unit‑size).
  • Custom model: You can't fine‑tune cohere.embed-multilingual-light-v3.0, so you can't create and host custom models built from that base.
Tip

Release and Retirement Dates

Model Release Date On-Demand Retirement Date Dedicated Mode Retirement Date
cohere.embed-multilingual-light-v3.0 2024-02-07 2026-01-22 2026-01-22
Important

For a list of all model time lines and retirement details, see Retiring the Models.

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.
  • In the Console, each input must be less than 512 tokens for the text only models.
  • 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.
  • For the text and image models, you can have files and inputs that all add up to 128,000 tokens.
  • For the text and image embed models, such as Cohere Embed English Image V3 you can either add text or add one image only. For the image, you can use API. Image input isn't available in the Console. For API, input a base64 encoded image in each run. For example, a 512 x 512 image is converted to about 1,610 tokens.

Learn about Creating text embeddings in OCI Generative AI.

Embedding Model Parameter

When using the embedding models, you can get a different output by changing the following parameter.

Truncate

Whether to truncate the start or end tokens in a sentence, when that sentence exceeds the maximum number of allowed tokens. For example, a sentence has 516 tokens, but the maximum token size is 512. If you select to truncate the end, the last 4 tokens of that sentence are cut off.