GPT2+CoreLM+Fine-Tuning
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
- Aristotle University of Thessaloniki
- Organisation type
- Academia
- Country
- Greece
- Published
- 4 November 2021
- Authors
- Nikolaos Stylianou, Ioannis Vlahavas
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Named entity recognition (NER)
- Base model
- GPT-2 (124M)
- Numerical format
- FP16
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
- 132M
- Training data
- tokens
- Epochs
- 10
"In all our experiments we use the GPT2-small configuration with 124M parameters, with 12 layers and 12 attention heads each for our base model." "Our model has 132M parameters, a 6% increase, after the addition of the Entity-Gating layer and the entity representations."
batch size of 128
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
- 1.2 × 10²⁰ FLOP
- How it was established
- Hardware
- Fine-tuning compute
- 2.6 × 10¹⁷ FLOP
GPT-2 124M compute ~ GPT-2 117M compute = 1.1699999999999999 × 10^20 FLOP 1.17 * 10^20 FLOP + 2.57472e+17 FLOP = 1.1725747e+20 FLOP
29800000000000 FLOP / s [Titan V, fp16 - reported] * 8 hours * 3600 sec / hour * 1 GPU * 0.3 [assumed utilization] = 2.57472e+17 FLOP (it is probably the upper bound since the 8 hours time seems to account for all models fine-tuned in the paper)
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 Titan V
- Chips used
- 1
- Wall-clock time
- 8 hours
- Power draw
- 277 W
"In this setup, fine-tuning takes approximately 8 hours"
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
- Likely
- Citations
- 2
- Benchmark data
- GPT2+CoreLM+Fine-Tuning
Sources
Where this record came from and when it was last checked.
- Reference
- CoreLM: Coreference-aware Language Model Fine-Tuning
- Last updated
- 28 November 2025
What the numbers mean
Background
GPT2+CoreLM+Fine-Tuning was published by Aristotle University of Thessaloniki, in the country recorded as Greece, during November 2021. The category the publisher falls under is academia.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Named entity recognition (NER).
Rather than being trained from scratch, it is derived from GPT-2 (124M). Most models at this scale are adapted from an existing base rather than built from nothing.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training run consumed about 1.2 × 10²⁰ FLOP, on hardware recorded as NVIDIA Titan V. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
GPT2+CoreLM+Fine-Tuning — common questions
GPT2+CoreLM+Fine-Tuning— how much compute was used to train it?
Training consumed around 1.2 × 10²⁰ FLOP, on hardware recorded as NVIDIA Titan V. 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.
GPT2+CoreLM+Fine-Tuning— 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.
GPT2+CoreLM+Fine-Tuning— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
GPT2+CoreLM+Fine-Tuning— how many parameters does it have?
It has a parameter count of 132M. "In all our experiments we use the GPT2-small configuration with 124M parameters, with 12 layers and 12 attention heads each for our base model." "Our model has 132M parameters, a 6% increase, after the addition of the Entity-Gating layer and the entity representations.". 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.
GPT2+CoreLM+Fine-Tuning— who created it?
It was published by Aristotle University of Thessaloniki, based in Greece, an organisation categorised as academia.
GPT2+CoreLM+Fine-Tuning— when was it released?
It was published in November 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
GPT2+CoreLM+Fine-Tuning— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Named entity recognition (NER). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.