Skip to page content
SAVRN
Search Plan a project

Open-weight model · Token classification

goby-4-ie-vi

by Belumind belumind/goby-4-ie-vi

goby-4-ie-vi is an open-weight model for token classification from Belumind, released under Apache License 2.0. It has 467M parameters. At 16-bit it needs about 1.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

Parameters467M
Context—
Weights—
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve goby-4-ie-vi (467M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.9 GB 1.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.5 GB 0.6 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.2 GB 0.3 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 20, 2026.

goby-4-ie-vi on every accelerator the SAVRN Index prices, at every precision

Model Card

Identity and Version

Repository
belumind/goby-4-ie-vi
Publisher
Belumind
Task
Token classification
Modality
Text
Library
gliner
Parameters
467M parameters
Languages
vi
Revision
b4f19d8ba6cefae00dbd92cfe651d2e19f76495c
First published
2026-09-20
Last updated
2026-09-21

License and Download

License
apache-2.0
Access
Open weights, no gate
Download from Belumind

Released by Belumind through its official repository on Hugging Face. Read the license.

Built From

  • Derived from knowledgator/gliner-relex-large-v1.0

Memory Requirements

PrecisionWeights in memory
16-bit0.9 GB
8-bit0.5 GB
4-bit0.2 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About goby-4-ie-vi

How much GPU memory does goby-4-ie-vi need?

About 1.1 GB at 16-bit and 0.3 GB at 4-bit: the weights (467M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run goby-4-ie-vi 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 goby-4-ie-vi commercially?

Yes. goby-4-ie-vi 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.

Similar Models

Model · Token classification

goby-9-ie-vi

Belumind

Joint entity + relation extraction for Vietnamese. The best-calibrated model in the goby family: higher precision at the same recall than its siblings. On clean text at each model own best threshold it ties goby-4 rather than beating it (see Measurement correction); on degraded text it is the worst of the family. Pick it for well-formed prose where you care about precision. Fine-tuned from knowledgator/gliner-relex-large-v1.0. Released by Belumind. Apache-2.0. Try it in the browser - no install, runs on ZeroGPU. goby-9 is not robust to degraded text - see Known limitations. That is not a small caveat, read it before choosing. Same training data as goby-4. The only difference is the loss…

Open weights apache-2.0 467M parameters gliner

Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for chemical entity recognition - chemical entities from the bc5cdr dataset. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research…

Open weights apache-2.0 434M parameters 512 tokens transformers

Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for disease entity recognition - disease entities from the bc5cdr dataset. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications.…

Open weights apache-2.0 434M parameters 512 tokens transformers

Model · Token classification

OpenMed-NER-ChemicalDetect-ModernMed-395M

OpenMed

Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for chemical entity recognition - identifies chemical compounds and substances in biomedical literature. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with…

Open weights apache-2.0 396M parameters 8,192 tokens transformers

Model · Token classification

OpenMed-NER-GenomeDetect-ModernMed-395M

OpenMed

Specialized model for Gene/Protein Entity Recognition - Gene and protein mentions This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for gene/protein entity recognition - gene and protein mentions. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. This model can…

Open weights apache-2.0 396M parameters 8,192 tokens transformers