Knowledge distillation student model
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
- Harvard University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 22 September 2016
- Authors
- Yoon Kim, Alexander M. Rush
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Translation
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
- 84M
- Training data
- 100,000,000 tokens
84M from Table 1.
"The training set has 4m sentences". If the average sentence is ~25 tokens (ballpark), dataset size is 4M * 25 * 2 = 200M tokens
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.
- Training compute
- 1 × 10¹⁷ FLOP
- How it was established
- Operation counting
6ND = 6 FLOP/param/token * 84000000 parameters * 200000000 tokens = 1.008e+17 FLOP (Speculative confidence since the amount of epochs is unknown)
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.
- Training code
- Open source
https://github.com/harvardnlp/seq2seq-attn?tab=readme-ov-file MIT License
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Sequence-Level Knowledge Distillation
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Knowledge distillation student model was published by Harvard University, in the country recorded as United States of America, during September 2016. The publishing organisation is categorised as academia.
It works in the domain of Language, and is recorded as performing the task of translation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Producing it required arithmetic totalling around 1 × 10¹⁷ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 100,000,000 tokens of text.
Answers
Knowledge distillation student model — common questions
Knowledge distillation student model— how much compute was used to train it?
Training consumed around 1 × 10¹⁷ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Knowledge distillation student model— 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.
Knowledge distillation student model— 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.
Knowledge distillation student model— how many parameters does it have?
It has a parameter count of 84M. 84M from Table 1. 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.
Knowledge distillation student model— who created it?
It was published by Harvard University, based in United States of America, an organisation categorised as academia.
Knowledge distillation student model— when was it released?
It was published in September 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Knowledge distillation student model— what is it used for?
It works in the domain of Language, and is recorded as handling the task of translation. 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.
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.