SeedLLM
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.
- Published
- 29 April 2024
- Authors
- Fan Yang, Huanjun Kong, Jie Ying, Zihong Chen, Tao Luo, Wanli Jiang, Zhonghang Yuan, Zhefan Wang, Zhaona Ma, Shikuan Wang, Wanfeng Ma, Xiaoyi Wang, Xiaoying Li, Zhengyin Hu, Xiaodong Ma, Minguo Liu, Xiqing Wang, Fan Chen, Nanqing Dong
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR)
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
To address these challenges, we introduce SeedLLM·Rice (SeedLLM), a 7-billion-parameter model trained on 1.4 million rice-related publications, representing nearly 98.24% of global rice research output.
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
- 我国首个种业大语言模型“丰登”发布
- Last updated
- 28 November 2025
What the numbers mean
What this model is
SeedLLM was published by its authors, during April 2024.
It works in the domain of Speech, and is recorded as performing the task of speech recognition (ASR).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
SeedLLM — common questions
SeedLLM— 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.
SeedLLM— how many parameters does it have?
It has a parameter count of 7B. To address these challenges, we introduce SeedLLM·Rice (SeedLLM), a 7-billion-parameter model trained on 1.4 million rice-related publications, representing nearly 98.24% of global rice research output. 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.
SeedLLM— when was it released?
It was published in April 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.
SeedLLM— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech recognition (ASR). These are the areas it was designed around; they describe intent rather than a hard boundary.
SeedLLM— 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.
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.