ToolFormer
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 9 February 2023
- Authors
- Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Base model
- GPT-J-6B
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
- 6.7B
- Training data
- tokens
Fine-tune of GPT-J-6B
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 A100 SXM4 40 GB
- Chips used
- 8
- Power draw
- 6.4 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
- 3,842
Sources
Where this record came from and when it was last checked.
- Reference
- Toolformer: Language Models Can Teach Themselves to Use Tools
- Last updated
- 25 May 2026
What the numbers mean
What this model is
ToolFormer was published by Meta AI, in United States of America, in February 2023. The organisation is categorised as industry.
It works in Language.
It builds on GPT-J-6B, which is why it shares that model's general shape and size.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
ToolFormer — common questions
How many parameters does ToolFormer have?
ToolFormer has 6.7B parameters. Fine-tune of GPT-J-6B. 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 ToolFormer?
ToolFormer was published by Meta AI, based in United States of America, categorised as industry.
When was ToolFormer released?
ToolFormer was published in February 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.
What is ToolFormer used for?
ToolFormer works in Language. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run ToolFormer?
None. ToolFormer 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 ToolFormer open source?
The licensing for ToolFormer 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.