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Open-weight model · Fill mask

ModernBERT-Large-Instruct-Logician-v0.5

by John Gorriceta JohnGorri/ModernBERT-Large-Instruct-Logician-v0.5

ModernBERT-Large-Instruct-Logician-v0.5 is an open-weight model for fill mask from John Gorriceta, released under Apache License 2.0. It has 396M parameters and a 8,192-token context. At 16-bit it needs about 1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

This model is a fine-tuned version of ModernBERT optimized for logical reasoning, deductive analysis, and structure-based token prediction.

Parameters396M
Context8,192
Weights791.8 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve ModernBERT-Large-Instruct-Logician-v0.5 (396M 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 0.8 GB 1.0 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.4 GB 0.5 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.2 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 7, 2026.

ModernBERT-Large-Instruct-Logician-v0.5 on every accelerator the SAVRN Index prices, at every precision

Model Card

By John Gorriceta, published under apache-2.0, revision b114c44d220f.

This model is a fine-tuned version of ModernBERT optimized for logical reasoning, deductive analysis, and structure-based token prediction. It relies on the ModernBertForMaskedLM architecture, making it highly effective at filling in missing contextual logic tokens (fill-mask). - As an encoder-based Masked Language Model, it is not designed for long-form generative text (like ChatGPT). It excels at predicting masked tokens inside structured prompts.

Read John Gorriceta's full model card

ModernBERT-Large-Instruct-Logician-v0

This model is a fine-tuned version of ModernBERT optimized for logical reasoning, deductive analysis, and structure-based token prediction. It relies on the ModernBertForMaskedLM architecture, making it highly effective at filling in missing contextual logic tokens (fill-mask).

Model Details

  • Developed by: JohnGorri
  • Model Type: Masked Language Model (MLM)
  • Base Model: answerdotai/ModernBERT
  • Language: English
  • License: Apache 2.0

Intended Uses & Limitations

Use Cases

  • Logical Deductions: Evaluating context clues to fill in missing arguments or qualifiers.

Limitations

  • As an encoder-based Masked Language Model, it is not designed for long-form generative text (like ChatGPT). It excels at predicting masked tokens inside structured prompts.

Configuration

Architecture
ModernBertForMaskedLM
Context length (tokens)
8,192
Layers
28
Hidden size
1,024
Feed-forward size
2,624
Attention heads
16
Vocabulary size
50,368
Model type
modernbert

Identity and Version

Repository
JohnGorri/ModernBERT-Large-Instruct-Logician-v0.5
Publisher
John Gorriceta
Task
Fill mask
Modality
Text
Library
Not stated by the source
Parameters
396M parameters
Languages
Not stated by the source
Revision
b114c44d220fd5e6bbea2de5ca1aa65e1d0095a2
First published
2026-09-21
Last updated
2026-09-21

Files and Weights

7 files, 795.4 MB in total. The weights are 2 files totalling 791.8 MB in bin, safetensors.

Weights2 files · 791.8 MB
Configuration1 file · 2.1 KB
Tokenizer2 files · 3.6 MB
Documentation1 file · 984 B
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights791.8 MB 114692ae04d8
training_args.binWeights5.2 KB 09e7908ee34b
config.jsonConfiguration2.1 KB —
README.mdDocumentation984 B —
.gitattributesRepository1.5 KB —
tokenizer.jsonTokenizer3.6 MB —
tokenizer_config.jsonTokenizer435 B —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
791.8 MB
Download from John Gorriceta

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

Built From

Memory Requirements

PrecisionWeights in memory
As published791.8 MB
16-bit0.8 GB
8-bit0.4 GB
4-bit0.2 GB

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

Built on This Model

Questions About ModernBERT-Large-Instruct-Logician-v0.5

How much GPU memory does ModernBERT-Large-Instruct-Logician-v0.5 need?

About 1 GB at 16-bit and 0.2 GB at 4-bit: the weights (396M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run ModernBERT-Large-Instruct-Logician-v0.5 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 ModernBERT-Large-Instruct-Logician-v0.5 commercially?

Yes. ModernBERT-Large-Instruct-Logician-v0.5 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.

What is ModernBERT-Large-Instruct-Logician-v0.5's context length?

8,192 tokens, from the maximum position embeddings in its published configuration.

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