EfficientNetV2-L (torchvision efficientnetv2l) fine-tuned for the Perturb subnet (netuid 26).
Open-weight model · Image classification
gvision-adv-uid141
by Nguyễn Trung Kiên kiennt120/gvision-adv-uid141
gvision-adv-uid141 is an open-weight model for image classification from Nguyễn Trung Kiên, released under Apache License 2.0. It has 119M parameters. At 16-bit it needs about 0.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
EfficientNetV2-L (torchvision efficientnetv2l) fine-tuned for the Perturb subnet (netuid 26).
Parameters119M
Context—
Weights476.3 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve gvision-adv-uid141 (119M 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 | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 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.
gvision-adv-uid141 on every accelerator the SAVRN Index prices, at every precision
Model Card
By Nguyễn Trung Kiên, published under apache-2.0, revision 7eb7d6e1a6b4.
EfficientNetV2-L (torchvision efficientnetv2l) fine-tuned for the Perturb subnet (netuid 26).
Read Nguyễn Trung Kiên's full model card
EfficientNetV2-L (torchvision efficientnet_v2_l) fine-tuned for the Perturb subnet (netuid 26).
- miner hotkey:
5D5YQSrV9ggZ9oKmYQfjhADzmrKdGgiu5RkxsbX6SoCsAhrs - on-chain hash: sha256(model.safetensors || hotkey) =
c3569d9e1887d3758eb9d124ca779f671759e9738a80637c398ebfb430223f26
Identity and Version
- Repository
- kiennt120/gvision-adv-uid141
- Publisher
- Nguyễn Trung Kiên
- Task
- Image classification
- Modality
- Image
- Library
- torchvision
- Parameters
- 119M parameters
- Languages
- Not stated by the source
- Revision
- 7eb7d6e1a6b4f8a7e37c875fe81661c7be50d1c1
- First published
- 2026-09-23
- Last updated
- 2026-09-23
Files and Weights
4 files, 476.3 MB in total. The weights are 1 file totalling 476.3 MB in safetensors.
Weights1 file · 476.3 MB
Configuration1 file · 606 B
Documentation1 file · 459 B
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 476.3 MB | 8a1c96f39dfa |
| config.json | Configuration | 606 B | — |
| README.md | Documentation | 459 B | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 476.3 MB
Download from Nguyễn Trung Kiên
Released by Nguyễn Trung Kiên through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 476.3 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About gvision-adv-uid141
How much GPU memory does gvision-adv-uid141 need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (119M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run gvision-adv-uid141 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 gvision-adv-uid141 commercially?
Yes. gvision-adv-uid141 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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