Xinyu
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
- Zhejiang University (ZJU),Institute for Advanced Algorithms Research,Northeastern University (China),China Telecom,State Key Laboratory of Media Convergence Production Technology and Systems
- Organisation type
- Academia,Academia,Academia,Industry,Academia
- Country
- China
- Published
- 23 August 2024
- Authors
- Yiquan Wu, Bo Tang, Chenyang Xi, Yu Yu, Pengyu Wang, Yifei Liu, Kun Kuang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Jie Hu, Peng Cheng, Zhonghao Wang, Yi Wang, Yi Luo, Mingchuan Yang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
- Base model
- Llama 2-13B
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
- 13B
- Training data
- 500,000,000 tokens
"we continued pre-training on LLaMA13B using a corpus comprising 500B tokens, which contains both English and Chinese corpus."
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- How it was established
- Hardware,Operation counting
- Fine-tuning compute
- 2.9 × 10²² FLOP
pre-training: 312000000000000 FLOP / GPU / sec [A800 80GB reported, bf 16 assumed] * 128 GPUs * 20 days * 24 hours / day * 3600 sec / hour * 0.3 [assumed utilization] = 2.0702822e+22 FLOP SFT: 312000000000000 FLOP / GPU / sec [A800 80GB reported, bf 16 assumed] * 8 GPUs * 2 days * 24 hours / day * 3600 sec / hour * 0.3 [assumed utilization] = 1.2939264e+20 FLOP Total: 2.0832215e+22 FLOP 6 FLOP / token / parameter * 13 * 10^9 parameters * 500 * 10^9 tokens = 3.9e+22 FLOP sqrt(3.9e+22*2.083…
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 A800 PCIe 80 GB
- Chips used
- 128
- Wall-clock time
- 528 hours (22 days)
- Power draw
- 63.1 kW
"The continued pre-training phase lasted 20 days on 128 Nvidia A800 80G GPUs, while the supervised fine-tuning (SFT) process took 2 days on 8 Nvidia A800 80G GPUs."
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
- Unreleased
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
- 5
Sources
Where this record came from and when it was last checked.
- Reference
- Xinyu: An Efficient LLM-based System for Commentary Generation
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Xinyu was published by Zhejiang University (ZJU),Institute for Advanced Algorithms Research,Northeastern University (China),China Telecom,State Key Laboratory of Media Convergence Production Technology and Systems, in China, in August 2024. The organisation is categorised as academia,Academia,Academia,Industry,Academia.
It works in Language, and is recorded as doing language modeling/generation.
It builds on Llama 2-13B, 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
It was trained on about 500,000,000 tokens of text.
Answers
Xinyu — common questions
Who created Xinyu?
Xinyu was published by Zhejiang University (ZJU),Institute for Advanced Algorithms Research,Northeastern University (China),China Telecom,State Key Laboratory of Media Convergence Production Technology and Systems, based in China, categorised as academia,Academia,Academia,Industry,Academia.
When was Xinyu released?
Xinyu was published in August 2024. 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 Xinyu used for?
Xinyu works in Language, and is recorded as handling 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 Xinyu?
None. Xinyu 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 Xinyu open source?
No. Xinyu has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Xinyu have?
Xinyu has 13B parameters. 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.
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.