Open-weight model
assignment-contrastive-learning50
by Kelvin Takahasi Takahasikelvin/assignment-contrastive-learning50
assignment-contrastive-learning50 is an open-weight model from Kelvin Takahasi, released under Creative Commons Attribution 4.0. It has 33,088 parameters and a 512-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.
Reading notes and an experiment sketch for Contrastive Learning. The repository emphasizes what still needs to be tested instead of manufacturing scores or release claims.
Runs On
What it takes to serve assignment-contrastive-learning50 (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 18, 2026.
assignment-contrastive-learning50 on every accelerator the SAVRN Index prices, at every precision
Model Card
By Kelvin Takahasi, published under cc-by-4.0, revision 8025bfbd3ed5.
Reading notes and an experiment sketch for Contrastive Learning. The repository emphasizes what still needs to be tested instead of manufacturing scores or release claims. - 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 include dataset versions, commands, seeds, hardware, and raw…
Read Kelvin Takahasi's full model card
Notes on Contrastive Learning
Repository summary
Reading notes and an experiment sketch for Contrastive Learning. The repository emphasizes what still needs to be tested instead of manufacturing scores or release claims.
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
- 8
- Hidden size
- 256
- Feed-forward size
- 1,024
- Attention heads
- 4
- Model type
- transformer
Identity and Version
- Repository
- Takahasikelvin/assignment-contrastive-learning50
- Publisher
- Kelvin Takahasi
- 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
- 8025bfbd3ed5d1c8ba48932eaf0c4d796289f1e7
- First published
- 2026-10-15
- Last updated
- 2026-10-15
Files and Weights
6 files, 139.5 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 | 441 B | — |
| training_args.json | Configuration | 188 B | — |
| README.md | Documentation | 1.5 KB | — |
| reading.md | Documentation | 3.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- cc-by-4.0
- Access
- Open weights, no gate
- Download size
- 132.8 KB
Released by Kelvin Takahasi 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 assignment-contrastive-learning50
How much GPU memory does assignment-contrastive-learning50 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 assignment-contrastive-learning50 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 assignment-contrastive-learning50 commercially?
Yes. assignment-contrastive-learning50 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 assignment-contrastive-learning50's context length?
512 tokens, from the maximum position embeddings in its published configuration.