Open-weight model
audio-visual-learning-reading
by Kenny J. Siregar sireken98/audio-visual-learning-reading
audio-visual-learning-reading is an open-weight model from Kenny J. Siregar, released under MIT License. It has 33,088 parameters and a 256-token context. At 16-bit it needs about 0 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
This repository contains a working research note about Audio Visual Learning. 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 audio-visual-learning-reading (33,088 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 Oct 23, 2026.
audio-visual-learning-reading on every accelerator the SAVRN Index prices, at every precision
Model Card
By Kenny J. Siregar, published under mit, revision 97c61c01d1eb.
This repository contains a working research note about Audio Visual Learning. 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 AudioSet and VGGSound - reproducibility checks, failure modes, and open questions - topic-relevant references Start with review.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…
Read Kenny J. Siregar's full model card
Notes on Audio Visual Learning
Repository summary
This repository contains a working research note about Audio Visual Learning. 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 AudioSet and VGGSound
- reproducibility checks, failure modes, and open questions
- topic-relevant references
How to read this repository
Start with review.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
review.md— primary artifactREADME.md— this documentation
License
Released under mit. Review the source-data terms separately when this repository is used with external datasets.
Configuration
- Architecture
- CustomResearchModel
- Context length (tokens)
- 256
- Layers
- 8
- Hidden size
- 256
- Feed-forward size
- 512
- Attention heads
- 2
- Model type
- transformer
Identity and Version
- Repository
- sireken98/audio-visual-learning-reading
- Publisher
- Kenny J. Siregar
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 33,088 parameters
- Languages
- Not stated by the source
- Revision
- 97c61c01d1eb6c76abddd6861732657cd8a84de9
- First published
- 2026-09-30
- Last updated
- 2026-09-30
Files and Weights
6 files, 139.6 KB in total. The weights are 1 file totalling 132.8 KB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 132.8 KB | e3990cda2590 |
| config.json | Configuration | 440 B | — |
| training_args.json | Configuration | 189 B | — |
| README.md | Documentation | 1.5 KB | — |
| review.md | Documentation | 3.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 132.8 KB
Released by Kenny J. Siregar through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 132.8 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 audio-visual-learning-reading
How much GPU memory does audio-visual-learning-reading need?
About 0 GB at 16-bit and 0 GB at 4-bit: the weights (33,088 parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run audio-visual-learning-reading 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 audio-visual-learning-reading commercially?
Yes. audio-visual-learning-reading is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
What is audio-visual-learning-reading's context length?
256 tokens, from the maximum position embeddings in its published configuration.