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Model release

LFM2.5-8B-A1B

LFM2.5-8B-A1B is an open-weight model release in the LFM (Liquid Foundation Models) family from Liquid AI14, version LFM2.5-8B-A1B, released May 28, 2026. Its weights can be downloaded by the public1 under the LFM Open License v1.021.

A text-only mixture-of-experts model with 8.3B total and 1.5B active parameters, built from 18 double-gated convolution blocks and 6 grouped-query attention blocks. Liquid AI pretrained it on 38 trillion tokens, extended its context to 128,000 tokens, and tuned it for reasoning; it writes a chain of thought before its final answer. It succeeds LFM2-8B-A1B.14

On AI on Hugging Face: LiquidAI/LFM2.5-8B-A1B

Last reviewedEntry updated Documented release May 28, 2026

Availability and license

Overall availability

Public

Weights download from Hugging Face without an access gate; use is governed by the LFM Open License v1.0, which conditions commercial-use rights on the licensee's legal entity not exceeding a threshold defined as annual revenue of US$10,000,000 or more. Liquid also offers the model in its Playground.124

Availability fact review: not separately recorded

Availability is separate from permission: read the license before using or redistributing.

Custom Liquid AI license (card license field "lfm1.0") modeled on Apache 2.0. It grants perpetual, royalty-free copyright and patent licenses, requires redistributors to include the license and mark modified files, and ends patent rights for anyone who files patent litigation over the work. Section 5 makes commercial rights conditional on the licensee's legal entity not exceeding a "Threshold" defined as annual revenue of US$10,000,000 or more, and states that commercial use by a legal entity that exceeds the Threshold is not licensed. The Threshold does not apply to a qualified non-profit organization's use for non-commercial or research purposes. Liquid's license page FAQ says fine-tuned models may be kept proprietary and that hosting platforms may host and distribute the models. The release post describes the weights as deployable without restrictions; the license text limits commercial use as described here.234

Component reuse rights

A readable or downloadable component is not automatically reusable. These indicators concern recorded license evidence, not system certification.
weights
Unknown — no complete fact-level rights review
code
Unknown — no complete fact-level rights review
data
Unknown — no complete fact-level rights review
documentation
Unknown — no complete fact-level rights review

No complete system-rights review is recorded for this release.

Model-disclosure tier

Computed from the checklist below using USASI rubric v0.2. An editorial category, not a certification.
Model-disclosure tier (USASI rubric v0.2): Open-weight

The model parameters for this release can be downloaded by the public. License terms may still restrict use, redistribution, or commercial use.

The weights are under a license that is not on the rubric's OSI-approved list. Read its terms before use.

How tiers are computed

Public materials checklist

Items for a model under USASI rubric v0.2. Unknown means unassessed or insufficient evidence.
Public materials checklist for LFM2.5-8B-A1B
ItemStatusNotes and evidence
WeightsCan the general public download the model parameters for this release?PublicUngated safetensors weights on Hugging Face; GGUF, ONNX, and MLX conversions are published as separate repositories.1
Inference codeIs code for running the model published?PublicThe model card documents inference with Hugging Face Transformers (5.0.0 or later), vLLM, SGLang, llama.cpp, MLX, and LM Studio; no custom modeling code is needed in the repository.1
Training codeIs the code used to train the model published?UnknownNo training code release was found in the model card, release post, or LFM2 technical report. Liquid documents fine-tuning with third-party tools (Unsloth, TRL), which is not training code for this release.
Training-data informationDoes the information cover provenance, scope, acquisition, selection, labeling, processing, and where data or alternatives can be obtained? Access alone does not establish completeness.UnknownNot assessed.
Training-data accessCan the training data be obtained? This is independent of information completeness and reuse rights; original unshareable data need not be downloadable.UnknownNot assessed.
Complete training pipelineIs the complete base-training and preprocessing pipeline published, including configuration? Fine-tuning code or an inference SDK alone is insufficient.UnknownNot assessed.
Legacy data assessment (v0.1)Historical assessment combining download access and disclosure. Preserved for traceability; excluded from the v0.2 tier calculation. See the new separate assessments above.UnknownThe model card and release post give the 38-trillion-token pretraining budget but do not describe data sources or composition.
Training recipeAre the training configuration and procedure documented in enough detail to follow?PartialThe release post outlines the stages at a high level (scaled-up pretraining, context extension to 32K and then 128K tokens, reasoning training, preference optimization against repetitive loops, and reward-based hallucination mitigation) without full configurations.4
Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only.PartialThe model card and release post report benchmark tables; evaluation code or prompts are not linked.14

What it is useful for

The model card recommends it for agentic workflows, tool use, structured outputs, multilingual assistants, and on-device personal-assistant applications, and says it is not the best fit for heavy programming or knowledge-intensive question answering without retrieval.1

Run and use notes

Documented facts only. No hardware or performance claims are made without a cited source and stated assumptions.
  • The model card recommends temperature 0.2, top_k 80, and repetition_penalty 1.05, and shows Transformers loading in bfloat16. It also points to a separate 328M speculative decoding drafter, LFM2.5-8B-A1B-DSpark, for use with SGLang.1

Organization context

Provenance and derivatives

Post-trained by Liquid AI from its own LFM2.5-8B-A1B-Base checkpoint, which it pretrained itself; no third-party base model is involved.1

Other releases in the LFM (Liquid Foundation Models) family

  • LFM2.5-2.6BModel-disclosure tier (USASI rubric v0.2): Open-weight
  • LFM2.5-24B-A2BModel-disclosure tier (USASI rubric v0.2): Open-weight

LFM (Liquid Foundation Models) family overview

What this catalog does not know

Unknown means the sources reviewed for this record do not document it. It is not evidence that something does not exist.
  • Training code: unknown.
  • Training-data information: unknown.
  • Training-data access: unknown.
  • Complete training pipeline: unknown.
  • Legacy data assessment (v0.1): unknown.

Have a primary source? How to report a correction.

U.S. eligibility

Project eligibility rests on documented governing or maintaining entities, not on contributors.

Eligible · basis: U.S. headquarters

Developed and published by Liquid AI, Inc., the licensor named in the model's license file. Liquid AI gives its address as Cambridge, Massachusetts (see the liquid-ai record).125

Assessed Sep 29, 2026

Sources

  1. 1.
    LiquidAI/LFM2.5-8B-A1B model card (external site: huggingface.co)

    Liquid AI · Model card · accessed Sep 29, 2026

  2. 2.
  3. 3.
    LFM License (external site: liquid.ai)

    Liquid AI · License · accessed Sep 29, 2026

  4. 4.
    LFM2.5-8B-A1B: An Even Better On-Device Mixture of Experts (external site: liquid.ai)

    Liquid AI · Announcement · published May 28, 2026 · accessed Sep 29, 2026

  5. 5.
    Liquid AI Privacy Policy (external site: liquid.ai)

    Liquid AI · Official page · published Jul 14, 2025 · accessed Sep 29, 2026

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APA style

USASI. (2026, September 29). LFM2.5-8B-A1B. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/lfm2-5-8b-a1b/

BibTeX

@misc{usasi_lfm2_5_8b_a1b,
  author = {{USASI}},
  title = {LFM2.5-8B-A1B},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/lfm2-5-8b-a1b/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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