Levelmodel
Classedge LLM
Parameters8.3B
Context128,000
ReleasedAug 2026
Opennessopen weights restricted
Runs onlaptop, mobile or NPU
Statusactive
Benchmark Results
vendor-reported
IFEvalknowledge and reasoning
vendor-reported
91.84
050100
Score in %. Higher is better.
MATH500math
vendor-reported
88.76
050100
Score in %. Higher is better.
Where this comes from
Every score names who ran it: vendor-reported, maintainer-run or independent. A rank is shown with the population it was ranked in (gap rule 3).
R&D Topics
Identity
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- hybrid 18 conv + 6 attention layers
- Total parameters
- 8.3B sources
- Active parameters
- 1.5B
- Checkpoint parameters
- –
- Context length
- 128,000 tokens
- Input
- text
- Output
- text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- open weights restricted
- Licence
- LFM Open License 1.0 (lfm1.0) sources
- Access routes
- open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- laptop, mobile or NPU
- Footprint
- GGUF, ONNX and MLX variants
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- 10 languages listed; no Indonesian
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
Developer
Liquid AIstartup, USA
Liquid AIstartup, USAModelLFM2.5-8B-A1BLiquid AI, edge LLM
LFM2.5-8B-A1BLiquid AI, edge LLMR&D TopicsEfficiency Architectures (MoE, Sparse Attention, Long Context)Quantization and Edge InferenceSmall Language Models (SLM)
Use CasesIndustrial Copilots and Maintenance AssistantsIndustrialIndonesian and Regional-Language LLMsPublic SectorTelecom Domain LLMs and BenchmarksTelecomUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Pertamina Geothermal EnergyIn production, 14 Jul 2026
Siemens Erlangen electronics factoryPlanned, 6 Jan 2026
ADNOC OffshoreIn production, 9 Sep 2025
Aker BPIn production, 9 Sep 2025+10 moreDeployments of these use cases; none names this model.
PT Pertamina Geothermal EnergyIn production, 14 Jul 2026Siemens Erlangen electronics factoryPlanned, 6 Jan 2026ADNOC OffshoreIn production, 9 Sep 2025Aker BPIn production, 9 Sep 2025+10 moreDeployments of these use cases; none names this model.Models 1
Organisations 1
R&D topics 3
Small Language Models (SLM)Models under about 10B parameters that fit consumer devices.produced byEfficiency Architectures (MoE, Sparse Attention, Long Context)Reducing training and inference cost per capability.produced byQuantization and Edge InferenceLower-bit weights and on-device serving.produced by
Sources
- huggingface.co/LiquidAI https://huggingface.co/LiquidAI
- huggingface.co/LiquidAI/LFM2.5-8B-A1B https://huggingface.co/LiquidAI/LFM2.5-8B-A1B
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "LFM2.5-8B-A1B", https://aurion.semai.id/models/liquid-ai-lfm2-5-8b-a1b/, records of October 2026.