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LFM2.5-350M
LFM2.5-350M is Liquid AI's 350-million-parameter text model for compact local and edge applications.
Liquid AI recommends it for extraction, structured outputs and tool use, and does not recommend it for knowledge-intensive work or programming. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
Compact instruction-tuned model
It supplies text generation to an application; it is not a ready-made assistant.
Why it stands out
Several deployment formats
Native weights, GGUF, ONNX, MLX and OpenVINO exports address different inference runtimes and hardware.
Availability
Model card and exports
Liquid AI's model page links the weights, formats and usage examples.
Why it matters
What makes it useful
A developer extracting fields from short records can use the model's documented structured-output path inside an application, then check the results against the records.
What to know
Where it fits
An inference runtime loads the selected export. The surrounding application supplies data and executes any requested tools; the model does not run those actions by itself. Default tool calls use a Python-style list. Liquid AI documents requesting JSON instead, so a JSON integration needs to set that instruction and handle the returned format.
Notable points
What stands out
The instruction-tuned model and the Base model serve different jobs. Liquid AI labels Base for fine-tuning, so the two downloads are not interchangeable choices for a conversational integration.
Before using
What to review
Choose an export supported by your runtime and device. Memory use depends on the export, precision and context in use; the parameter count alone does not show whether your application will fit.
Check extraction results and tool requests; a small model or structured format does not establish correctness.
Reader fit
Who may find it relevant
Developers working on bounded text-processing tasks with constrained deployment targets.
It needs an inference setup and application integration; Liquid AI's stated limits matter for broader knowledge or coding tasks.
Editorial note
Why LifeHubber lists it
A small local model does not have to answer every question from its training data to be useful. LFM2.5 supports tool definitions supplied by the application, so a bounded task can draw on an external function's current result—for example, a weather reading—while keeping the model's job to requesting and interpreting that result. That is a useful role for this compact model, provided the application checks the request and controls what the function may do.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Plan the rest of a local model setup.
A compact model is one component. Continue with the runtime, hardware and data-path choices that determine how a local AI workflow actually runs.
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