LLaMA-13B (LoRA finetuned)
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
- Organisation type
- Industry
- Country
- Korea (Republic of)
- Published
- 23 May 2023
- Authors
- Jeonghoon Kim, Jung Hyun Lee, Sungdong Kim, Joonsuk Park, Kang Min Yoo, Se Jung Kwon, Dongsoo Lee
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering
- Base model
- LLaMA-13B
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
- 13B
- Training data
- tokens
- Epochs
- 3
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Benchmark data
- LLaMA-13B (LoRA finetuned)
Sources
Where this record came from and when it was last checked.
- Reference
- Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization
- Last updated
- 11 February 2026
What the numbers mean
Background
LLaMA-13B (LoRA finetuned) was published by NAVER, in Korea (Republic of), in May 2023. industry is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling/generation, Question answering.
Its starting point was LLaMA-13B — most models at this scale are adapted from an existing base rather than built from nothing.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
LLaMA-13B (LoRA finetuned) — common questions
When was LLaMA-13B (LoRA finetuned) released?
LLaMA-13B (LoRA finetuned) 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.
What is LLaMA-13B (LoRA finetuned) used for?
LLaMA-13B (LoRA finetuned) works in Language, and is recorded as handling language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run LLaMA-13B (LoRA finetuned)?
None. LLaMA-13B (LoRA finetuned) 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.
Is LLaMA-13B (LoRA finetuned) open source?
No. LLaMA-13B (LoRA finetuned) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does LLaMA-13B (LoRA finetuned) have?
LLaMA-13B (LoRA finetuned) has 13B parameters. 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.
Who created LLaMA-13B (LoRA finetuned)?
LLaMA-13B (LoRA finetuned) was published by NAVER, based in Korea (Republic of), categorised as industry.
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.