TAIWAN-LLM 7B
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
- National Taiwan University
- Organisation type
- Academia
- Country
- Taiwan
- Published
- 23 November 2023
- Authors
- Yen-Ting Lin, Yun-Nung Chen
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Chat
- Base model
- Llama 2-7B
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
- 7B
- Training data
- tokens
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA H100 SXM5 80GB
- Chips used
- 48
- Power draw
- 66.6 kW
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 34
Sources
Where this record came from and when it was last checked.
- Reference
- "TAIWAN-LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model"
- Last updated
- 25 May 2026
What the numbers mean
Background
TAIWAN-LLM 7B was published by National Taiwan University, in the country recorded as Taiwan, during November 2023. The category the publisher falls under is academia.
It works in the domain of Language, and is recorded as performing the task of chat.
Its starting point was an existing base model, Llama 2-7B. That is the usual way a specialised model is produced.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
TAIWAN-LLM 7B — common questions
TAIWAN-LLM 7B— how many parameters does it have?
It has a parameter count of 7B. 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.
TAIWAN-LLM 7B— who created it?
It was published by National Taiwan University, based in Taiwan, an organisation categorised as academia.
TAIWAN-LLM 7B— when was it released?
It was published in November 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.
TAIWAN-LLM 7B— what is it used for?
It works in the domain of Language, and is recorded as handling the task of chat. 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.
TAIWAN-LLM 7B— 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.
TAIWAN-LLM 7B— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.