LMSI-Palm
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
- Google,University of Illinois Urbana-Champaign (UIUC)
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 20 October 2022
- Authors
- Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language generation, Language modeling/generation, Question answering, Mathematical reasoning
- Base model
- PaLM (540B)
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
- 540B
- Training data
- 1,920,000 tokens
540B
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.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 851
Table 3 "We show that our approach improves the general reasoning ability of a 540B-parameter LLM (74.4%->82.1% on GSM8K, 78.2%->83.0% on DROP, 90.0%->94.4% on OpenBookQA, and 63.4%->67.9% on ANLI-A3) and achieves state-of-the-art-level performance, without any ground truth label."
Sources
Where this record came from and when it was last checked.
- Reference
- Large Language Models Can Self-Improve
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
LMSI-Palm was published by Google,University of Illinois Urbana-Champaign (UIUC), in United States of America, in October 2022. The organisation is categorised as industry,Academia.
It works in Language, and is recorded as doing language generation, Language modeling/generation, Question answering, Mathematical reasoning.
It builds on PaLM (540B), which is why it shares that model's general shape and size.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
It was trained on about 1,920,000 tokens of text.
Its inclusion criterion is sOTA improvement.
Answers
LMSI-Palm — common questions
What is LMSI-Palm used for?
LMSI-Palm works in Language, and is recorded as handling language generation, Language modeling/generation, Question answering, Mathematical reasoning. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run LMSI-Palm?
None. LMSI-Palm 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 LMSI-Palm open source?
No. LMSI-Palm has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does LMSI-Palm have?
LMSI-Palm has 540B parameters. 540B. 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 LMSI-Palm?
LMSI-Palm was published by Google,University of Illinois Urbana-Champaign (UIUC), based in United States of America, categorised as industry,Academia.
When was LMSI-Palm released?
LMSI-Palm was published in October 2022. 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.