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Local AI for bioinformatics ​

Liatir runs supported scientific AI Models locally through signed Runtime Boxes. A Runtime Box contains the model weights, Python interpreter, packages, and Liatir runner that were tested together. Liatir does not install model packages into your system Python.

AI Models and AI Tools ​

An AI Model is the packaged scientific model. An AI Tool is the task that sends an input to a compatible installed model and turns its output into Jobs, Results, files, and provenance.

The current product catalog contains:

AI ModelCurrent published targetsMain use
Geneformer V1 10MmacOS arm64 Metal; Linux CUDA 12.9; Windows CUDA 12.8human single-cell embeddings
scGPT Whole-humanmacOS arm64 Metal; Linux/Windows CPU; Linux CUDA 12.9; Windows CUDA 12.8human single-cell embeddings
UCE 4-layermacOS arm64 Metalmulti-species single-cell embeddings

The current AI Tool is Single-cell Embedding. No other AI Model or AI Tool is available until it has its own reviewed, published Runtime Box path.

Installation ​

  1. Open AI Models.
  2. Choose a model compatible with the detected computer.
  3. Select Install.
  4. Liatir downloads the target-specific Runtime Box, verifies its signature and hashes, and activates it atomically.
  5. The installed model becomes available to direct runs and pipelines.

AI Models are installed once per device and are available to all workspaces. Removing a model removes its activated Runtime Box and reclaims its disk space.

Input requirements ​

All three models accept AnnData .h5ad files with raw scRNA-seq counts in .X, but gene identifiers differ:

  • Geneformer is human-only and expects Ensembl IDs in var["ensembl_id"] or var_names.
  • scGPT is human-only and expects gene symbols in var["gene_name"], var["feature_name"], or var_names.
  • UCE supports the species listed in its run form and expects gene symbols in var_names.

Outputs ​

A successful run creates:

  • an embedded AnnData file;
  • a lightweight CSV preview;
  • a JSON summary;
  • model-specific intermediate artifacts when required;
  • Runtime Box provenance identifying the exact box release and native target.

The embedded AnnData file is the main scientific artifact. An embedding is a numeric representation for comparison, clustering, visualization, or later analysis; it is not a final biological conclusion.

Hardware support ​

Liatir installs only a published target that exactly matches the operating system, architecture, accelerator, memory, and driver constraints. Native Windows CUDA support is limited to the exact published CUDA 12.8 Geneformer and scGPT targets; other Windows CUDA combinations are not inferred from them.