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Model family

LFM (Liquid Foundation Models)

LFM (Liquid Foundation Models) is a model family from Liquid AI13, with 3 assessed releases in this catalog. Licenses and availability are recorded for each release, not for the family; its releases use the LFM Open License v1.0.

LFM is Liquid AI's series of hybrid language and multimodal models for on-device use, built from gated short-convolution blocks and a small number of grouped-query attention blocks. LFM2 (from July 2025) introduced open-weight dense models from 350M parameters upward and later an 8.3B mixture-of-experts model. LFM2.5 (from January 2026) extended pretraining and scaled up reinforcement-learning post-training; 2026 releases include LFM2.5-8B-A1B (May), LFM2.5-2.6B (August), and LFM2.5-VL-3B (August), alongside vision, audio, and encoder variants.43516710

On AI on Hugging Face: LiquidAI/LFM2.5-VL-3B

Last reviewedEntry updated Documented release Unknown

Releases assessed

Each release is assessed on its own. This family has no family-wide openness label.
Releases in the LFM (Liquid Foundation Models) family
ReleaseWeightsTier (USASI rubric v0.2)LicenseReleased
LFM2.5-2.6BWeights: PublicModel-disclosure tier (USASI rubric v0.2): Open-weightLFM Open License v1.0Aug 4, 2026
LFM2.5-24B-A2BWeights: PublicModel-disclosure tier (USASI rubric v0.2): Open-weightLFM Open License v1.0Oct 13, 2026
LFM2.5-8B-A1BWeights: PublicModel-disclosure tier (USASI rubric v0.2): Open-weightLFM Open License v1.0May 28, 2026

What it is useful for

Liquid AI positions the models for on-device and other resource-constrained deployment, and its model cards recommend the current text models for agentic workflows, tool use, data extraction, retrieval-augmented generation, and structured outputs rather than knowledge-heavy or heavy coding tasks.289

Organization context

Provenance and derivatives

The LFM language models are pretrained by Liquid AI itself; the current text instruction and reasoning models are post-trained from Liquid's own base checkpoints (for example LFM2.5-2.6B-Base and LFM2.5-8B-A1B-Base). The vision-language models are not text-only LFMs: the LFM2.5-VL-3B model card says it combines the LFM2.5-2.6B language backbone with a SigLIP2 NaFlex vision encoder. Liquid's LFM2 announcement says an earlier Liquid model, LFM1-7B, was used as a teacher for knowledge distillation during LFM2 training.89114

In the news

Dated, sourced updates in this catalog's news that mention this entry.
  1. · Release

    Liquid AI releases LFM2.5-24B-A2B, an open-weight mixture-of-experts model for on-device agents

    Liquid AI announced LFM2.5-24B-A2B on October 13, 2026, describing it as a reasoning model built for fast, private agents on-device and as the largest and final model in the LFM2.5 family. The release includes a base model (LFM2.5-24B-A2B-Base) and a post-trained model (LFM2.5-24B-A2B), published as open weights on Hugging Face. The model card lists 24.0B total parameters with 2.45B active per token and a context length of 131,072 tokens, and says the model is not recommended for knowledge-heavy tasks on its own. The weights are released under the LFM Open License v1.0. USASI has not tested this model.

  2. · Release

    Liquid AI releases open-weight d1 decision models for text, vision, and audio

    On October 7, 2026 Liquid AI announced "Open d1", two open-weight decision models that return an answer in a single forward pass instead of generating text. The announcement and model cards describe d1-3B, built on LFM2.5-VL-3B, which takes text and images, and d1-omni-600M, built on LFM2.5-Encoder-350M, which takes text with images or text with up to 30 seconds of speech. The weights are published on Hugging Face, and the announcement says the models have day-one support in llama.cpp. Both model cards give the license as "other" with the name lfm1.0, pointing to a LICENSE file in each repository; the announcement itself does not name a license.

  3. · Release

    Liquid AI releases LFM2.5-VL-3B-DSpark, a draft model for faster vision-language decoding

    Liquid AI announced LFM2.5-VL-3B-DSpark on September 24, 2026. It is an experimental draft model of about 280 million parameters that speeds up text generation by its LFM2.5-VL-3B vision-language model through speculative decoding; Liquid AI says the target model's output is unchanged. The weights are on Hugging Face in Safetensors and GGUF formats, with support in SGLang, MLX-VLM, and llama.cpp. They are released under the LFM Open License v1.0, the same license as other LFM2.5 releases, which limits commercial use by larger companies through an annual-revenue threshold.

  4. · Release

    Liquid AI releases LFM2.5-VL-3B, an open-weight vision-language model for on-device use

    Liquid AI announced LFM2.5-VL-3B on August 12, 2026, a non-reasoning vision-language model that it says answers directly to keep latency low for real-time and on-device applications. The model card says it uses the LFM2.5-2.6B language model as its backbone with a SigLIP2 NaFlex vision encoder, and lists a context length of 32,768 tokens. Weights are on Hugging Face with GGUF, ONNX, and MLX exports, under the LFM Open License v1.0. USASI has not tested this model.

  5. · Release

    Liquid AI releases LFM2.5-2.6B, an open-weight on-device model trained for agentic workloads

    Liquid AI announced LFM2.5-2.6B on August 4, 2026, describing it as a 2.6B-parameter model trained for agentic workloads such as planning, calling tools, and multi-step tasks on devices including phones and CPUs. The base model (LFM2.5-2.6B-Base) and the post-trained model (LFM2.5-2.6B) were published on Hugging Face. The model card lists a context length of 131,072 tokens and says the model is not recommended for agentic coding and knowledge-heavy tasks. The weights are released under the LFM Open License v1.0. USASI has not tested this model.

U.S. eligibility

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

Eligible · basis: U.S. headquarters

The LFM models are developed and published by Liquid AI, Inc., which gives its address as Cambridge, Massachusetts (see the liquid-ai record). Eligibility of each release is assessed on the release record.129

Assessed Sep 29, 2026

Sources

  1. 1.
    Models | Liquid AI (external site: liquid.ai)

    Liquid AI · Official page · accessed Sep 29, 2026

  2. 2.
    Welcome | Liquid AI documentation (external site: docs.liquid.ai)

    Liquid AI · Documentation · accessed Sep 29, 2026

  3. 3.
    LFM2 Technical Report (arXiv 2511.23404) (external site: arxiv.org)

    Liquid AI · Paper · published Nov 28, 2025 · accessed Sep 29, 2026

  4. 4.
    Introducing LFM2: The Fastest On-Device Foundation Models on the Market (external site: liquid.ai)

    Liquid AI · Announcement · published Jul 10, 2025 · accessed Sep 29, 2026

  5. 5.
    Introducing LFM2.5: The Next Generation of On-Device AI (external site: liquid.ai)

    Liquid AI · Announcement · published Jan 5, 2026 · accessed Sep 29, 2026

  6. 6.
    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

  7. 7.
    LFM2.5-2.6B: Deploy Agents Everywhere (external site: liquid.ai)

    Liquid AI · Announcement · published Aug 4, 2026 · accessed Sep 29, 2026

  8. 8.
    LiquidAI/LFM2.5-2.6B model card (external site: huggingface.co)

    Liquid AI · Model card · accessed Sep 29, 2026

  9. 9.
    LiquidAI/LFM2.5-8B-A1B model card (external site: huggingface.co)

    Liquid AI · Model card · accessed Sep 29, 2026

  10. 10.
    LFM2.5-VL-3B: A Better and Faster Vision-Language Model for the Edge (external site: liquid.ai)

    Liquid AI · Announcement · published Aug 12, 2026 · accessed Sep 29, 2026

  11. 11.
    LiquidAI/LFM2.5-VL-3B model card (external site: huggingface.co)

    Liquid AI · Model card · accessed Sep 29, 2026

  12. 12.
    Liquid AI Privacy Policy (external site: liquid.ai)

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

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USASI. (2026, September 29). LFM (Liquid Foundation Models). United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/lfm/

BibTeX

@misc{usasi_lfm,
  author = {{USASI}},
  title = {LFM (Liquid Foundation Models)},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/lfm/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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