Practical overview
When it fits
Use the LFModel example to explore model construction and a forward pass with numeric tensors; use the Liquid Transformer example to inspect the experimental attention, liquid-cell and MoE combination.
Who it is for
- Python developers exploring Liquid Foundation Model implementations.
- Researchers or builders inspecting an experimental combination of transformer attention, liquid cells and mixture-of-experts layers.
Documented uses
- Construct an LFModel with configurable dimensions and expert count.
- Pass a numeric PyTorch tensor through LFModel.
- Instantiate the experimental Liquid Transformer with configurable architecture parameters and inspect its output tensor.
Requirements & limits
The implementation is published as a public GitHub repository under the MIT License. The documented installation command is pip3 install -U lfm-torch.
- The repository explicitly says it is an independent open-source implementation, not the official Liquid AI repository.
- The LFModel example uses PyTorch and numeric tensors for a forward pass.
- The Liquid Transformer is described as very experimental and early.
- The README says the experimental transformer still needs an actual tokenizer; tokenizer integration and compatibility are not demonstrated.
- The README describes a training script as in progress rather than presenting a completed training workflow.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Lfm and construct an LFModel for a forward pass
Use the README’s documented package installation and example to construct LFModel and pass it a PyTorch tensor.
Before you start
- Python environment with pip3
- PyTorch, imported as torch in the example
- Package installed with pip3 install -U lfm-torch
Steps
Install the package with pip3 install -U lfm-torch.
Import torch and LFModel from lfm_torch.model.
Set the dimensions and expert count, construct LFModel, and create a three-dimensional random tensor as shown in the README.
Pass the tensor to the model to perform the example forward pass.
Expected outcome
The example sends a numeric tensor through LFModel and logs that the forward pass is complete.
Guide limitations
- The example demonstrates a forward pass with numeric tensors, not a complete text-generation workflow.
- The README does not specify supported operating systems.
Construct the experimental Liquid Transformer and pass a tensor
Follow the README’s example to construct LiquidTransformer and run its shown tensor forward pass.
Before you start
- Python environment with the lfm-torch package installed
- PyTorch, imported as torch in the example
Steps
Import LiquidTransformer from lfm_torch.liquid_t_moe and import torch.
Set the example sequence length, batch size, embedding size, head count, expert count, expert size and layer count.
Construct LiquidTransformer with those parameters.
Create the example tensor and pass it to the model.
Expected outcome
The example produces a model output tensor and logs its shape.
Guide limitations
- The README labels the architecture very experimental and early.
- The README says an actual tokenizer is still needed; this example uses a numeric tensor.
- The README describes a training script as in progress and does not provide a completed training workflow here.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- kyegomez/LFM · Official homepage title · retained in its source language