Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
voyage-large-2-instruct Embedding Model Free trial
By:
Latest Version:
v1
Instruction-tuned general-purpose embedding model optimized for retrieval, classification, clustering, and reranking. 16K context length.
Product Overview
Text embedding models are neural networks that transform texts into numerical vectors. They are a crucial building block for semantic search/retrieval systems and retrieval-augmented generation (RAG) and are responsible for the retrieval quality. voyage-large-2-instruct is a cutting-edge general-purpose embedding model and is among the top in the overall MTEB leaderboard, outperforming OpenAI v3 large and Cohere English v3 on key tasks, such as retrieval, classification, clustering, and reranking. Further, voyage-large-2-instruct is trained to be responsive to additional instructions that are prepended to the input text. Latency is 90 ms for a single query with at most 100 tokens, and throughput is 12.6M tokens per hour at $0.22 per 1M tokens on an ml.g6.xlarge. Learn more about voyage-large-2-instruct here: https://blog.voyageai.com/2024/05/05/voyage-large-2-instruct-instruction-tuned-and-rank-1-on-mteb/
Key Data
Version
Type
Model Package
Highlights
Superior general-purpose capability and is among the top in the overall MTEB leaderboard , outperforming OpenAI v3 large and Cohere English v3 on key tasks, such as retrieval, classification, clustering, and reranking.
Responsive to additional prepended instructions. For retrieval/search tasks (e.g., in RAG), use the
[input_type](https://docs.voyageai.com/docs/faq#when-and-how-should-i-use-the-input_type-parameter)
parameter. For classification, clustering, or other MTEB subtasks, please use the instructions here .16K token context length, well-suited for applications on long documents. Latency is 90 ms for a single query with at most 100 tokens. 12.6M tokens per hour at $0.22 per 1M tokens on an ml.g6.xlarge.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
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Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$2.112/hr
running on ml.g5.xlarge
Model Batch Transform$0.00/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$1.408/host/hr
running on ml.g5.xlarge
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
About Free trial
Try this product for 7 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.g5.xlarge Vendor Recommended | $2.112 | |
ml.g5.8xlarge | $4.59 | |
ml.g5.2xlarge | $2.2725 | |
ml.g5.4xlarge | $3.045 | |
ml.g5.16xlarge | $7.68 |
Usage Information
Model input and output details
Input
Summary
input
:str
orList[str]
- Single text or list of texts.input_type
:str
, optional (default=None
) - May also be"query"
or"document"
.truncation
:bool
, optional (default=True
) -True
: Truncates.False
: raises error if any given text exceeds the context length.encoding_format
:str
, optional (default=None
) - Embedding format.None
: float list;"base64"
: compressed encoding.
Limitations for input type
The maximum number of tokens for each text is 16K, the maximum length of the list is 128, and the total number of tokens in the list is at most 32K.
Input MIME type
text/csv, application/json, application/jsonlinesSample input data
Output
Summary
The API will respond with a JSON object containing a list of embedding objects, along with other information such as model name and token usage.
Output MIME type
application/json, text/csv, application/jsonlinesSample output data
Sample notebook
Additional Resources
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Support Information
voyage-large-2-instruct Embedding Model
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