CoRe
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
- Tsinghua University
- Organisation type
- Academia
- Country
- China
- Published
- 29 December 2023
- Authors
- Xinyu Zhu, Junjie Wang, Lin Zhang, Yuxiang Zhang, Ruyi Gan, Jiaxing Zhang, Yujiu Yang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Mathematics, Language
- Task
- Quantitative reasoning, Language modeling/generation
- 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
- 12.4B
- Training data
- tokens
"Since the default setting consists of two GPT-J (6B) and a DeBERTa-large (0.4B), we note our backbone as “GPT-J 12B”, which implies around 12.4 billion parameters in total. "
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
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
- Open (non-commercial)
no clear license https://github.com/TianHongZXY/CoRe
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
- Citations
- 109
We evaluate our CoRe framework on several mathematical reasoning datasets and achieve decent improvement over state-of-the-art methods, up to 9.6% increase over best baselines.
Sources
Where this record came from and when it was last checked.
- Reference
- Solving Math Word Problems via Cooperative Reasoning induced Language Models
- Last updated
- 25 May 2026
What the numbers mean
Background
CoRe was published by Tsinghua University, in China, in December 2023. The organisation is categorised as academia.
It works in Mathematics, Language, and is recorded as doing quantitative reasoning, Language modeling/generation.
It builds on GPT-J-6B, 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
The reason it appears in this catalogue at all is sOTA improvement.
Answers
CoRe — common questions
How many parameters does CoRe have?
CoRe has 12.4B parameters. "Since the default setting consists of two GPT-J (6B) and a DeBERTa-large (0.4B), we note our backbone as “GPT-J 12B”, which implies around 12.4 billion parameters in total. ". 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 CoRe?
CoRe was published by Tsinghua University, based in China, categorised as academia.
When was CoRe released?
CoRe was published in December 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 CoRe used for?
CoRe works in Mathematics, Language, and is recorded as handling quantitative reasoning, Language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run CoRe?
None. CoRe 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 CoRe open source?
No. CoRe has not had its weights published, so it exists only as a service controlled by its owner.
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.