LMRec
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- NAVER,Naver AI Lab
- Organisation type
- Industry,Industry
- Country
- Korea (Republic of)
- Published
- 13 May 2023
- Authors
- Kyuyong Shin, Hanock Kwak, Wonjae Kim, Jisu Jeong, Seungjae Jung, Kyung-Min Kim, Jung-Woo Ha, Sang-Woo Lee
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Recommendation
- Task
- Language modeling/generation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 210M
- Training data
- 12,998,864,057 tokens
- Batch size
- 1,024
210 million (Figure 4)
Table 3 1222700000 (in-house dataset) + 347300000 (piblic) = 1,570,000,000 tokens
table 9
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 2 × 10¹⁸ FLOP
- How it was established
- Operation counting
210000000*1570000000*6=1.9782e+18
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning
- Last updated
- 28 November 2025
What the numbers mean
Background
LMRec was published by NAVER,Naver AI Lab, in the country recorded as Korea (Republic of), during May 2023. It comes out of an organisation categorised as industry,Industry.
It works in the domain of Language, Recommendation, and is recorded as performing the task of language modeling/generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required arithmetic totalling around 2 × 10¹⁸ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 12,998,864,057 tokens of text.
Answers
LMRec — common questions
LMRec— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
LMRec— how many parameters does it have?
It has a parameter count of 210M. 210 million (Figure 4). That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
LMRec— who created it?
It was published by NAVER,Naver AI Lab, based in Korea (Republic of), an organisation categorised as industry,Industry.
LMRec— when was it released?
It was published in May 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
LMRec— what is it used for?
It works in the domain of Language, Recommendation, and is recorded as handling the task of language modeling/generation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
LMRec— how much compute was used to train it?
Training consumed around 2 × 10¹⁸ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
LMRec— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.