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Model reference · open weights

LFM2-RAG

LLMs LiquidAI Text gen 1 build Its own licence terms 2k dl/mo

LFM2-RAG is an open-weight language model from LiquidAI. LFM2-1.2B-RAG-GGUF (GGUF) weighs 731 MB; the smallest configuration that runs it is RTX 3060 12 GB.

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  • LFM2-RAG is a 1.2B parameter text-generation model developed by LiquidAI and published as LiquidAI/LFM2-1.2B-RAG-GGUF.
  • It is specialized for answering questions based on provided contextual documents within Retrieval-Augmented Generation systems.
  • The model supports English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish, and is distributed under an other licence.

Summary of the LiquidAI/LFM2-1.2B-RAG-GGUF model card, 2026-10-01

Hugging Face ↗

What it is

Released byLiquidAI
Released2025-09-05
VRAM731 MB for the weights

What it runs on

Memory and cards for LFM2-1.2B-RAG-GGUF (GGUF)

731 MBweights, file size
651 MBruntime overhead, at least

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GBfits
RTX 4060 Ti 16 GBfits
RTX 3090 24 GBfits
RTX 4090 24 GBfits
RTX 5090 32 GBfits
L40S 48 GBfits
A100 80 GBfits
H100 80 GBfits
RTX PRO 6000 Blackwell 96 GBfits
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits

From the model card

What LiquidAI says about LFM2-RAG

Read the model card

src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" alt="Liquid AI" style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;" />

LFM2-1.2B-RAG-GGUF

Based on LFM2-1.2B, LFM2-1.2B-RAG is specialized in answering questions based on provided contextual documents, for use in RAG (Retrieval-Augmented Generation) systems.

Use cases:

  • Chatbot to ask questions about the documentation of a particular product.
  • Custom support with an internal knowledge base to provide grounded answers.
  • Academic research assistant with multi-turn conversations about research papers and course materials.

You can find more information about other task-specific models in this blog post.

🏃 How to run LFM2

Example usage with llama.cpp:

llama-cli -hf LiquidAI/LFM2-1.2B-RAG-GGUF

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.
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