Sold by: Liquid AI
Deployed on AWS
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Liquid LFM 7B is designed to handle complex tasks, offering an optimal balance between size and output quality.
Overview
LFM-7B is specifically optimized for response quality, accuracy, and usefulness. To assess its chat capabilities, we leverage a diverse frontier LLM jury to compare responses generated by LFM-7B against other models in the 7B-8B parameter category. It allows us to reduce individual biases and produce more reliable comparisons.
Highlights
- **Innovative Model Architecture**: Liquid AI's Foundation Models utilize a unique architecture that combines liquid neural networks and non-transformer designs, allowing these models to be efficient in memory usage and capable of handling sequential data, such as text, video, and real-time signals. This setup optimizes performance while minimizing computational demands.
- **Enhanced Adaptability and Real-Time Learning**: Unlike conventional models, LFMs can adapt their internal processes based on new inputs in real time, making them highly responsive.
- **Efficiency in Long-Context Processing**: Liquid AI's models can efficiently process extended input sequences without the steep memory and processing requirements typical of transformer-based models, supporting applications like document summarization and complex chatbot interactions with minimal hardware demands. With LFMs, it’s possible to fit up to 1 million tokens-worth of data and map it onto 16 gigabytes of memory. **
Details
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Latest version
Deployed on AWS
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Pricing
Free trial
Try this product free for 7 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 | $10.00 |
ml.g6e.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6e.2xlarge instance type, real-time mode | $3.00 |
ml.g6e.xlarge Inference (Real-Time) | Model inference on the ml.g6e.xlarge instance type, real-time mode | $3.00 |
ml.g6e.4xlarge Inference (Real-Time) | Model inference on the ml.g6e.4xlarge instance type, real-time mode | $3.00 |
ml.g6e.16xlarge Inference (Real-Time) | Model inference on the ml.g6e.16xlarge instance type, real-time mode | $3.00 |
ml.g6e.8xlarge Inference (Real-Time) | Model inference on the ml.g6e.8xlarge instance type, real-time mode | $3.00 |
Dimensions summary
You pay by the hour for each running instance, billed as HostHrs. Pricing scales with the compute instance you choose, not by tokens or requests. Five instances run in real-time mode, ranging from the ml.g6e.xlarge up through the ml.g6e.16xlarge. Larger instance sizes carry higher hourly rates. One instance, the ml.g4dn.12xlarge, runs in batch mode for processing grouped workloads. You select the instance and mode that fit your latency and throughput needs, then pay only for the hours it runs.
Top-of-mind questions for buyers
One HostHrs equals one hour that a chosen instance runs the model. You pay per hour per active instance. Billing follows the compute instance, not the number of tokens generated or requests served. Each running instance accrues its own hourly charge separately.
Charges apply per hour while an instance runs. If you stop or terminate an instance, the software hourly charge stops accruing. Underlying AWS infrastructure fees, like storage, may still apply based on your AWS account. The model charge meters running time only.
Real-time mode runs on five ml.g6e instance sizes and serves live requests with low latency. Batch mode runs on the ml.g4dn.12xlarge instance and processes grouped workloads together. Both bill per HostHrs. Choose real-time for responsive apps and batch for high-volume grouped processing.
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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
Initial version
Additional details
Inputs
- Summary
The model leverages OpenAI's chat format as detailed in OpenAI API documentation , with the following key specifics:
- Supports text-only interactions.
- Mandates the model parameter to be explicitly set to /opt/ml/model for proper functionality.
- Input MIME type
- application/json
https://github.com/Liquid4All/aws-samples/blob/main/notebooks/sagemaker/data/input/real-time/simple.json
https://github.com/Liquid4All/aws-samples/blob/main/notebooks/sagemaker/data/input/real-time/simple.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Request | https://platform.openai.com/docs/api-reference/chat | Type: FreeText | Yes |
Resources
Vendor resources
Support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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