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v2.3 · 2026-08-09
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LFM2.5 Embedding 350M

lfm2-5-embedding-350m
EMBEDDING

LFM2.5 Embedding 350M is a 0.354B embedding model profile for local AI planning. This page records provider, license, context, quantization, hardware-fit estimates, setup hints, source links, and caveats so readers do not choose a model by name alone.

PROVIDER
Liquid AI
FAMILY
LFM2.5
MODEL TYPE
embedding
PARAMETERS
0.354B
MODALITIES
text
ARCHITECTURE
Bidirectional bi-encoder with CLS-style single-vector pooling
CONTEXT WINDOW
512 tokens
TRAINING TOKENS
—
RELEASE DATE
2026-06-18
LICENSE & LINKS
LFM Open License v1.0license text
GGUF REPOSITORIES
Official GGUF repository for llama.cpp-compatible retrieval.
SETUP HINTS
LLAMA.CPP
Use the official GGUF repository with current embedding support.
RAM / VRAM ESTIMATES
MIN RAM
4 GB
COMFORTABLE RAM
8 GB
These are conservative local-inference estimates, not official hardware requirements.
HARDWARE FIT
CPU only (no GPU)
yes
8 GB RAM
comfortable
16 GB RAM
comfortable
32 GB RAM
comfortable
8 GB VRAM
not required
12 GB VRAM
not required
24 GB VRAM
not required
Apple Silicon (unified memory)
comfortable
Hardware fit values are conservative local-inference estimates based on GGUF size plus runtime and KV-cache overhead. Actual requirements depend on context length, quantization, runtime, GPU offload, and other running apps.
BEST FOR
·Fast multilingual semantic search with a compact single-vector index.
AVOID IF
·You need long-document encoding without chunking or maximum retrieval accuracy regardless of index size.
CAVEATS
·Use the model card's query and document prefixes; omitting them can reduce retrieval quality.
·The 512-token document limit requires chunking longer material.
·Vendor benchmark and latency figures have not been reproduced by LocalLLMGuide.com.
·The custom license includes a commercial-use revenue threshold.
SOURCE URLS
FIELD EVIDENCE
trainingTokens—
LANGUAGES
ArabicGermanEnglishSpanishFrenchItalianJapaneseKoreanNorwegianPortugueseSwedish
CAPABILITIES
embedding model
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