BAPO 32B
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
- Fudan University,Shanghai Qiji Zhifeng
- Organisation type
- Academia,Industry
- Country
- China
- Published
- 21 October 2025
- Authors
- Zhiheng Xi, Xin Guo, Yang Nan, Enyu Zhou, Junrui Shen, Wenxiang Chen, Jiaqi Liu, Jixuan Huang, Zhihao Zhang, Honglin Guo, Xun Deng, Zhikai Lei, Miao Zheng, Guoteng Wang, Shuo Zhang, Peng Sun, Rui Zheng, Hang Yan, Tao Gui, Qi Zhang, Xuanjing Huang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering
- Base model
- Qwen2.5-32B
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
- 32B
- Training data
- tokens
32B
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/WooooDyy/BAPO
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- BAPO: Stabilizing Off-Policy Reinforcement Learning for LLMs via Balanced Policy Optimization with Adaptive Clipping
- Last updated
- 28 November 2025
What the numbers mean
What this model is
BAPO 32B was published by Fudan University,Shanghai Qiji Zhifeng, in China, in October 2025. It comes out of academia,Industry.
It works in Language, and is recorded as doing language modeling/generation, Question answering.
Its starting point was Qwen2.5-32B — most models at this scale are adapted from an existing base rather than built from nothing.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
BAPO 32B — common questions
What is BAPO 32B used for?
BAPO 32B works in Language, and is recorded as handling language modeling/generation, Question answering. 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.
What GPU do I need to run BAPO 32B?
None. BAPO 32B 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 BAPO 32B open source?
No. BAPO 32B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does BAPO 32B have?
BAPO 32B has 32B parameters. 32B. 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 BAPO 32B?
BAPO 32B was published by Fudan University,Shanghai Qiji Zhifeng, based in China, categorised as academia,Industry.
When was BAPO 32B released?
BAPO 32B was published in October 2025.
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.