Sold by: LightOnÂ
Overview
Mini-instruct is a powerful, multilingual AI model with 40B parameters trained on high-quality data from a variety of sources. It is built using Falcon LLM technology from the Technology Innovation Institute.
The amount of data in Mini corpus is 1 trillion tokens. We mainly used data from the public web to pre-train our model, with strong filtering, toxicity reduction, and deduplication to ensure that only high-quality data is retained.
It's designed to understand natural language and respond to instructions tailored to your needs. It works great in consumer products, such as chatbots, voice assistants, and smart appliances. It also has broad applications in the enterprise, such as natural language generation for automated customer service or agent assist for customer support.
The API documentation, resources on prompt design and parameters are available in the documentation: https://lightonai.github.io/paradigm-docs/Â
Highlights
- Apowerful, multilingual AI model with 40B parameters trained on 1 trillion tokens of high-quality data from a variety of sources.
- Text generation, keyword extraction, information extraction, question answering, summarization, sentiment analysis, classification
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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Try this product free for 30 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.12xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $11.00 |
ml.p4d.24xlarge Inference (Real-Time) Recommended | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $11.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
- instruction-tuned 40B large language model
Additional details
Inputs
- Summary
The model accepts JSON-formatted input following the specifications provided in the developer documentation for each endpoint https://lightonai.github.io/paradigm-docs/apiÂ
- Limitations for input type
- The API accepts JSON files that are at most 6MB
- Input MIME type
- application/json
{
"data":
{
"text": "Generate a list of ideas for articles on watercolour.\n1. Watercolour in history.\n2.",
"params": {
"n_tokens": 50,
"temperature": 0.9,
"seed": 0
}
},
"endpoint":"llm/create"
}
https://lightonai.github.io/paradigm-docs/api/specifications/requests#batching-requests
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
endpoint | Choice of endpoint among Create, Analyse, Select and Tokenize. | Type: FreeText | Yes |
Resources
Vendor resources
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