Token-classification checkpoint used to produce the reference entities in It is CyNER (Alam et al., 2022) fine-tuned on 50 MITRE ATT&CK software articles annotated with five STIX-aligned labels. Every entity-based number in the paper (answer-matching precision/recall, frequency-based entity similarity, the Threatpost and The Hacker News ground truth, the Llama-3 summary NER) was produced by this exact checkpoint, so it is released to make those numbers regenerable. BIO tagging over five entity types: Label definitions follow Table 3 of the paper (Malware, System, Indicator, Vulnerability, Organization). Fine-tuned with the CyNER training loop (cyner.TransformersNER) from xlm-roberta-base.…
Open-weight model · Token classification
actividad10-ner-xlmr
by Yorquin Yobany Rojas Silva yobanyr/actividad10-ner-xlmr
actividad10-ner-xlmr is an open-weight model for token classification from Yorquin Yobany Rojas Silva. It has 277M parameters. At 16-bit it needs about 0.7 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Parameters277M
Context—
Weights—
License—
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve actividad10-ner-xlmr (277M 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 | 0.6 GB | 0.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.3 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.2 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 22, 2026.
actividad10-ner-xlmr on every accelerator the SAVRN Index prices, at every precision
Model Card
Identity and Version
- Repository
- yobanyr/actividad10-ner-xlmr
- Publisher
- Yorquin Yobany Rojas Silva
- Task
- Token classification
- Modality
- Text
- Library
- Not stated by the source
- Parameters
- 277M parameters
- Languages
- es
- Revision
- 47aafd526c8ab6e93ba137491a1e0f58dded6b41
- First published
- 2026-10-19
- Last updated
- 2026-10-19
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
Download from Yorquin Yobany Rojas Silva
Released by Yorquin Yobany Rojas Silva through its official repository on Hugging Face.
Built From
- Derived from FacebookAI/xlm-roberta-base
Memory Requirements
| Precision | Weights in memory |
|---|---|
| 16-bit | 0.6 GB |
| 8-bit | 0.3 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About actividad10-ner-xlmr
How much GPU memory does actividad10-ner-xlmr need?
About 0.7 GB at 16-bit and 0.2 GB at 4-bit: the weights (277M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run actividad10-ner-xlmr 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.
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