BITTERS
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
- LG,Shutterstock
- Organisation type
- Industry,Industry
- Country
- Korea (Republic of), United States of America
- Published
- 1 October 2023
- Authors
- Taehoon Kim, Mark Marsden, Pyunghwan Ahn, Sangyun Kim, Sihaeng Lee, Alessandra Sala, Seung Hwan Kim
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image captioning
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
- 650M
- Training data
- 108,800,000,000 tokens
BITTERS has 650 million parameters in total.
we train BITTERS using a new, quality-controlled 100 million image dataset which we will refer to as Text Image Pairs 100 Million (TIP100M) WaveVAE module We also resize each image to 256 × 256 × 3 We train the model for 3 epochs with a batch size of 480 Batch size: 1280 Number of updates: 2 epochs Image resolution: 256x256 Sequence length: 64 text tokens, 1024 image 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
- 7.8 × 10¹⁷ FLOP
- How it was established
- Operation counting
WaveVAE: "We train the model for 10 epochs with batch size 3840." "We reduced the number of parameters to 25 million in total" 25000*100000000*6*10=1.5e+14 BiART: "We train our model for 2 epochs in total with batch size 1280" "BITTERS has 650 million parameters in total" 650000000*100000000*6*2=7.8e+17 7.8e+17+1.5e+14=7.8015e+17
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
Sources
Where this record came from and when it was last checked.
- Reference
- Large-Scale Bidirectional Training for Zero-Shot Image Captioning
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
BITTERS was published by LG,Shutterstock, in the country recorded as Korea (Republic of), during October 2023. The publishing organisation is categorised as industry,Industry.
It works in the domain of Vision, and is recorded as performing the task of image captioning.
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 7.8 × 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 108,800,000,000 tokens of text.
Answers
BITTERS — common questions
BITTERS— when was it released?
It was published in October 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
BITTERS— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image captioning. 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.
BITTERS— how much compute was used to train it?
Training consumed around 7.8 × 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.
BITTERS— 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.
BITTERS— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
BITTERS— how many parameters does it have?
It has a parameter count of 650M. BITTERS has 650 million parameters in total. 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.
BITTERS— who created it?
It was published by LG,Shutterstock, based in Korea (Republic of), an organisation categorised as industry,Industry.
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.