2nd order FOFE-FNNLM
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
- University of Science and Technology of China (USTC),York University
- Organisation type
- Academia,Academia
- Country
- China, Canada
- Published
- 6 May 2015
- Authors
- Shiliang Zhang, Hui Jiang, Mingbin Xu, Junfeng Hou, Lirong Dai
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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
- 6M
- Training data
- tokens
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.
- Citations
- 18
- Benchmark data
- 2nd order FOFE-FNNLM
Sources
Where this record came from and when it was last checked.
- Reference
- A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models
- Last updated
- 28 November 2025
What the numbers mean
About this model
2nd order FOFE-FNNLM was published by University of Science and Technology of China (USTC),York University, in the country recorded as China, during May 2015. The publishing organisation is categorised as academia,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
2nd order FOFE-FNNLM — common questions
2nd order FOFE-FNNLM— how many parameters does it have?
It has a parameter count of 6M. 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.
2nd order FOFE-FNNLM— who created it?
It was published by University of Science and Technology of China (USTC),York University, based in China, an organisation categorised as academia,Academia.
2nd order FOFE-FNNLM— when was it released?
It was published in May 2015. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
2nd order FOFE-FNNLM— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
2nd order FOFE-FNNLM— 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.
2nd order FOFE-FNNLM— is it open source?
No. Its weights have not been 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.