EfficientNetV2-L (torchvision efficientnetv2l) fine-tuned for the Perturb subnet (netuid 26).
slate-heron is an open-weight model for image classification from Ivan Marahovschi, 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, 1000 ImageNet classes) fine-tuned with adversarial training 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 slate-heron (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.
slate-heron on every accelerator the SAVRN Index prices, at every precision
Model Card
By Ivan Marahovschi, published under apache-2.0, revision 5b4d62aec215.
EfficientNetV2-L (torchvision efficientnetv2l, 1000 ImageNet classes) fine-tuned with adversarial training for the Perturb subnet (netuid 26).
Read Ivan Marahovschi's full model card
EfficientNetV2-L (torchvision efficientnet_v2_l, 1000 ImageNet classes) fine-tuned with adversarial
training for the Perturb subnet (netuid 26).
- miner hotkey:
5FGxe3AFaJ3LsM5LJtxdBS8143WFEd9eNWVKErr29uV1K8wt - on-chain hash: sha256(model.safetensors || hotkey) =
cf646ad2d3d83147992faaca9ca4bd5a7c2242b9a11330aa6f7189095e0dbf5d - preprocessing:
EfficientNet_V2_L_Weights.IMAGENET1K_V1.transforms()(bicubic resize 480, center crop 480, mean = std = 0.5)
from safetensors.torch import load_file
from torchvision.models import efficientnet_v2_l
model = efficientnet_v2_l(weights=None)
model.load_state_dict(load_file("model.safetensors"))
Identity and Version
- Repository
- qhoenix/slate-heron
- Publisher
- Ivan Marahovschi
- Task
- Image classification
- Modality
- Image
- Library
- torchvision
- Parameters
- 119M parameters
- Languages
- Not stated by the source
- Revision
- 5b4d62aec215881e19e66a84c8d944925ec093ac
- First published
- 2026-09-30
- Last updated
- 2026-09-30
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 · 424 B
Documentation1 file · 850 B
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 476.3 MB | d51a1e917834 |
| config.json | Configuration | 424 B | — |
| README.md | Documentation | 850 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 Ivan Marahovschi
Released by Ivan Marahovschi 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 slate-heron
How much GPU memory does slate-heron 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 slate-heron 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 slate-heron commercially?
Yes. slate-heron 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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