ALM 1.0
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
- Beijing Academy of Artificial Intelligence / BAAI
- Organisation type
- Academia
- Country
- China
- Published
- 28 November 2022
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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
- 335M
- Training data
- 22,696,572,400 tokens
335M parameters: https://github.com/FlagAI-Open/FlagAI/blob/master/examples/ALM/README.md
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
- Hosted access (no API)
- Training code
- Unreleased
It seems to have only inference and finetuning codes, no weights or pretraining code https://github.com/FlagAI-Open/FlagAI/tree/master/examples/ALM
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
- Speculative
SOTA results on Arabic-language benchmark ALUE.
Sources
Where this record came from and when it was last checked.
- Reference
- ALM 1.0
- Last updated
- 28 November 2025
What the numbers mean
What this model is
ALM 1.0 was published by Beijing Academy of Artificial Intelligence / BAAI, in the country recorded as China, during November 2022. The publishing organisation is categorised as academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training set ran to roughly 22,696,572,400 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
ALM 1.0 — common questions
ALM 1.0— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. 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.
ALM 1.0— 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.
ALM 1.0— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
ALM 1.0— how many parameters does it have?
It has a parameter count of 335M. 335M parameters: https://github.com/FlagAI-Open/FlagAI/blob/master/examples/ALM/README.md. 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.
ALM 1.0— who created it?
It was published by Beijing Academy of Artificial Intelligence / BAAI, based in China, an organisation categorised as academia.
ALM 1.0— when was it released?
It was published in November 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.