LIMA
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
- Meta AI,Carnegie Mellon University (CMU),University of Southern California,Tel Aviv University
- Organisation type
- Industry,Academia,Academia,Academia
- Country
- United States of America, Israel
- Published
- 18 May 2023
- Authors
- Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, Omer Levy
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Chat, Language modeling/generation
- 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
- 65B
- Training data
- 685,300 tokens
- Epochs
- 15
"We train LIMA (Less Is More for Alignment) using the following protocol. Starting from LLaMa 65B [Touvron et al., 2023], we fine-tune on our 1,000-example alignment training set," according to page 4 of https://arxiv.org/pdf/2305.11206.
The total amount of training data is roughly 750,000 tokens, split over exactly 1,000 sequences.
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
- 5.5 × 10²³ FLOP
- How it was established
- Operation counting
- Fine-tuning compute
- 4.4 × 10²⁰ FLOP
Finetune: 4.39e18 FLOP Base model: 5.5e+23 FLOP Total: 4.39e18+5.5e+23=5.5e+23 FLOP
“The total amount of training data is roughly 750,000 tokens, split over exactly 1,000 sequences,” according to page 2 of https://arxiv.org/pdf/2305.11206. “We train LIMA (Less Is More for Alignment) using the following protocol. Starting from LLaMa 65B [Touvron et al., 2023], we fine-tune on our 1,000-example alignment training set. To differentiate between each speaker (user and assistant), we introduce a special end-of-turn token (EOT) at the end of each utterance; this token plays the same …
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.
- Likely above 10²³ FLOP
- Yes
- Record confidence
- Confident
- Citations
- 1,274
Sources
Where this record came from and when it was last checked.
- Reference
- LIMA: Less Is More for Alignment
- Last updated
- 25 May 2026
What the numbers mean
What this model is
LIMA was published by Meta AI,Carnegie Mellon University (CMU),University of Southern California,Tel Aviv University, in United States of America, in May 2023. industry,Academia,Academia,Academia is the category the publisher falls under.
It works in Language, and is recorded as doing chat, Language modeling/generation.
Its starting point was LLaMA-65B — most models at this scale are adapted from an existing base rather than built from nothing.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Producing it required around 5.5 × 10²³ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
The training set ran to roughly 685,300 tokens.
Answers
LIMA — common questions
What is LIMA used for?
LIMA works in Language, and is recorded as handling chat, Language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train LIMA?
Around 5.5 × 10²³ 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.
What GPU do I need to run LIMA?
None. LIMA 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 LIMA open source?
No. LIMA has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does LIMA have?
LIMA has 65B parameters. "We train LIMA (Less Is More for Alignment) using the following protocol. Starting from LLaMa 65B [Touvron et al., 2023], we fine-tune on our 1,000-example alignment training set," according to page 4 of https://arxiv.org/pdf/2305.11206. 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 LIMA?
LIMA was published by Meta AI,Carnegie Mellon University (CMU),University of Southern California,Tel Aviv University, based in United States of America, categorised as industry,Academia,Academia,Academia.
When was LIMA released?
LIMA 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.
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.