LLaMA-65B (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-65B
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
- 65.2B
- Training data
- tokens
- Epochs
- 5
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-65B (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-65B (LoRA finetuned) was published by NAVER, in the country recorded as Korea (Republic of), during May 2023. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Question answering.
Its starting point was an existing base model, LLaMA-65B. That is why it shares the base model's general shape and size.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
LLaMA-65B (LoRA finetuned) — common questions
LLaMA-65B (LoRA finetuned)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
LLaMA-65B (LoRA finetuned)— how many parameters does it have?
It has a parameter count of 65.2B. 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.
LLaMA-65B (LoRA finetuned)— who created it?
It was published by NAVER, based in Korea (Republic of), an organisation categorised as industry.
LLaMA-65B (LoRA finetuned)— 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.
LLaMA-65B (LoRA finetuned)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
LLaMA-65B (LoRA finetuned)— 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.