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LFM3: Choosing Lightweight Text, Vision and Speech Models for Small Teams

Liquid AI introduced the LFM3 model family on October 27, covering text, vision, audio and other directions, and providing open weights. This article combines official releases and model cards to explain how to select versions for different input tasks, and what results should be recorded during local trials. The capabilities are introduced from the manufacturer, and independent performance testing has not been done. Commercial use requires checking the specific license.

Chinese edition published 29 Oct 2026 · 阅读中文原文 ↗

Source timingSource release date: 2026-10-27; data verification: October 29, 2026. The model card and license page are not marked with a fixed release date and should be read according to the current version.

A family, first distinguished by input and output

Liquid AI's official team article positions LFM3 as a lightweight model family for specialized tasks. The version introduced this time is on the order of 2B parameters, including text, visual, audio and comprehensive multi-modal directions. "Lightweight" here describes the size of the model and cannot be directly deduced to mean that any computer can run smoothly.

In the official model card, LFM3-1.7B is used for text generation, and LFM3-VL-2B is used for image and text input and text output. The audio and comprehensive multimodal versions in the family are geared toward different forms of input and output. When using it, you should check the model card of the downloaded version, and do not count the capabilities of the entire family into one file.

ChinaOPC Selection Suggestion: First fix a job with an answer

Assuming that the customer hopes to extract the model from the product description, the test set should contain examples of normal models, multiple models in parallel, without model and information conflicts, and the expected results should be confirmed by business personnel in advance. When you need to identify packaging photos, then evaluate the visual version; if the task only involves organized text, first check whether the text model meets the requirements.

This test plan is a verification method proposed by ChinaOPC, not a completed evaluation. During the evaluation, you can record "correct extraction", "leave blank if missing" and "prompt for verification in case of conflict" separately to avoid just looking at whether the language is fluent. For small teams, knowing which situations must be returned to manual labor is often more helpful in arranging delivery than a comprehensive ranking list.

Record the entire processing process on the target device

The local trial should write down the model name, file version, quantification format, running tools and equipment configuration, and then record the complete time from submitting materials to getting usable results. The time required to open the model, the wait during continuous processing, and the failure caused by the abnormal file should be kept separately, and not just one smooth demonstration can be shown.

If you want to provide continuous service, you can add simultaneous requests, task interruptions and manually modified retries. Check whether the form or text finally received by the customer is available, count rework and maintenance investment. This article does not run these models on the specified hardware and therefore does not list speed, accuracy, memory footprint, or cost savings promises.

Check the opening weight and usage license separately

The text and visual model cards checked are labeled with the lfm1.0 license and linked to the manufacturer's license. The manufacturer's instructions include conditions for commercial use, and "can be downloaded" cannot be understood to mean that it can be used unconditionally in all business scenarios. Before delivering it to the customer, you should check the license attached to the model file actually used and its complete text, and keep a version record; this article does not replace the license judgment for specific uses.

For products that require only one fixed task, you can first use a small-scale sample to judge whether the model can be completed stably, and then decide whether it is worth investing in fine-tuning and deployment. You can also compare other tools with the AI Model Column, but the same batch of materials and the same acceptance requirements should be used to avoid misleading by different demonstration conditions.

Sources and editorial notes

AI-assisted organizing and writing, and the listed public sources have been checked. ChinaOPC analyzed and simulated scenarios independently and did not conduct personal interviews, customer test orders, financial audits or actual model testing. Quoting facts does not imply authorization to reprint the full text or original images; this article does not reprint the full text of the source or use its images. English translation of the Chinese edition, prepared with machine assistance and editorial checks. Source verification dates refer to the original Chinese research.

Source titles and publisher names are retained in their original language. Refer to the linked originals for official documents and current requirements.

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