DPO on Pythia-2.8B
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
- Stanford University,CZ Biohub Network
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 29 May 2023
- Authors
- Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering
- Base model
- Pythia-2.8b
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
- 2.8B
- Training data
- tokens
same as base model
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
- Open source
"DPO is relatively straightforward to implement; PyTorch code for the DPO loss is provided [in the paper]"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Historical significance
- Record confidence
- Confident
the paper introduced DPO
Sources
Where this record came from and when it was last checked.
- Reference
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model
- Last updated
- 28 November 2025
What the numbers mean
About this model
DPO on Pythia-2.8B was published by Stanford University,CZ Biohub Network, in United States of America, in May 2023. It comes out of academia,Academia.
It works in Language, and is recorded as doing language modeling/generation, Question answering.
Its starting point was Pythia-2.8b — 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.
How it was trained
Its inclusion criterion is historical significance.
Answers
DPO on Pythia-2.8B — common questions
How many parameters does DPO on Pythia-2.8B have?
DPO on Pythia-2.8B has 2.8B parameters. same as base model. 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.
Who created DPO on Pythia-2.8B?
DPO on Pythia-2.8B was published by Stanford University,CZ Biohub Network, based in United States of America, categorised as academia,Academia.
When was DPO on Pythia-2.8B released?
DPO on Pythia-2.8B was published in May 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.
What is DPO on Pythia-2.8B used for?
DPO on Pythia-2.8B works in Language, and is recorded as handling language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run DPO on Pythia-2.8B?
None. DPO on Pythia-2.8B 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.
Is DPO on Pythia-2.8B open source?
No. DPO on Pythia-2.8B has not had its weights 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.