Open-weight model
reading-self-supervised-2023
by Lucia Fernandez luciafernandez/reading-self-supervised-2023
This repository contains a working research note about Self Supervised. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models.
Runs On
What it takes to serve reading-self-supervised-2023 (16,576 parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
By Lucia Fernandez, published under cc-by-4.0, revision 8397f2e6bf36.
This repository contains a working research note about Self Supervised. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with reading.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should…
Read Lucia Fernandez's full model card
Notes on Self Supervised
Repository summary
This repository contains a working research note about Self Supervised. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models.
What is covered
- the scope of the research question and likely confounders
- a proposed comparison with matched baselines
- concrete evaluation context such as task-appropriate public benchmarks named in the main note
- reproducibility checks, failure modes, and open questions
- topic-relevant references
How to read this repository
Start with reading.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw logs.
Scope and limitations
The note is intentionally exploratory. It does not claim benchmark improvements, completed ablations, released code, or a trained checkpoint. References and proposed datasets provide a starting point for verification rather than evidence that the study has already been run.
Files
reading.md— primary artifactREADME.md— this documentation
License
Released under cc-by-4.0. Review the source-data terms separately when this repository is used with external datasets.
Configuration
- Architecture
- CustomResearchModel
- Context length (tokens)
- 512
- Layers
- 4
- Hidden size
- 128
- Feed-forward size
- 256
- Attention heads
- 8
- Model type
- transformer
Identity and Version
- Repository
- luciafernandez/reading-self-supervised-2023
- Publisher
- Lucia Fernandez
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 16,576 parameters
- Languages
- Not stated by the source
- Revision
- 8397f2e6bf3632bffb2dfbca22712b6d8fee232c
- First published
- 2026-09-18
- Last updated
- 2026-09-18
Files and Weights
6 files, 73.6 KB in total. The weights are 1 file totalling 66.8 KB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 66.8 KB | 4ed61401de93 |
| config.json | Configuration | 440 B | — |
| training_args.json | Configuration | 189 B | — |
| README.md | Documentation | 1.5 KB | — |
| reading.md | Documentation | 3.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- cc-by-4.0
- Access
- Open weights, no gate
- Download size
- 66.8 KB
Released by Lucia Fernandez through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 66.8 KB |
| 16-bit | 0.0 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About reading-self-supervised-2023
How much GPU memory does reading-self-supervised-2023 need?
About 0 GB at 16-bit and 0 GB at 4-bit: the weights (16,576 parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run reading-self-supervised-2023 on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use reading-self-supervised-2023 commercially?
Yes. reading-self-supervised-2023 is released under Creative Commons Attribution 4.0. CC BY 4.0 permits sharing and adapting the work, including commercially, provided the creator is credited and changes are indicated.
What is reading-self-supervised-2023's context length?
512 tokens, from the maximum position embeddings in its published configuration.