QueryCategorizer is an open-weight model from Najaf Ali. It has 67M parameters and a 512-token context. At 16-bit it needs about 0.2 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Runs On
What it takes to serve QueryCategorizer (67M 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 | 0.1 GB | 0.2 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 | 0.0 GB | 0.0 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 Sep 19, 2026.
QueryCategorizer on every accelerator the SAVRN Index prices, at every precision
Model Card
The publisher has not written a card for this model.
Configuration
- Architecture
- DistilBertForSequenceClassification
- Context length (tokens)
- 512
- Vocabulary size
- 30,522
- Model type
- distilbert
Identity and Version
- Repository
- NajafAli01/QueryCategorizer
- Publisher
- Najaf Ali
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 67M parameters
- Languages
- Not stated by the source
- Revision
- d3e2445e20afa6882008dc2825f164ddb0cfbb17
- First published
- 2026-09-19
- Last updated
- 2026-09-19
Files and Weights
6 files, 268.6 MB in total. The weights are 2 files totalling 267.9 MB in bin, safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 267.9 MB | 65973c20bd19 |
| training_args.bin | Weights | 5.2 KB | 0962203f9569 |
| config.json | Configuration | 1.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 711.6 KB | — |
| tokenizer_config.json | Tokenizer | 351 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 267.9 MB
Released by Najaf Ali through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 267.9 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About QueryCategorizer
How much GPU memory does QueryCategorizer need?
About 0.2 GB at 16-bit and 0 GB at 4-bit: the weights (67M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run QueryCategorizer 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.
What is QueryCategorizer's context length?
512 tokens, from the maximum position embeddings in its published configuration.