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Open-weight model · Text classification
by Kshitij Vinayaksing Rajput Nebulixlabs/Quantum-Classifier-Calibrated
Quantum-Classifier-Calibrated is an open-weight model for text classification from Kshitij Vinayaksing Rajput, released under Apache License 2.0. It has 1M parameters and a 128-token context. At 16-bit it needs about 0 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 14 downloads a month.
Nebulixlabs/Quantum-Classifier 0 = safe 1 = unsafe The held-out test split, where available, is evaluated only after fine-tuning.
What it takes to serve Quantum-Classifier-Calibrated (1M 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 | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 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 Oct 18, 2026.
Quantum-Classifier-Calibrated on every accelerator the SAVRN Index prices, at every precision
By Kshitij Vinayaksing Rajput, published under apache-2.0, revision c2d9abf4c2c2.
Nebulixlabs/Quantum-Classifier 0 = safe 1 = unsafe The held-out test split, where available, is evaluated only after fine-tuning.
Fine-tuned version of:
Nebulixlabs/Quantum-Classifier
Datasets:
Batch size:
128
Target tokens:
30,000,000
Actual tokens:
30,010,756
Epochs:
9
0 = safe
1 = unsafe
Temperature calibrated on validation data:
1.479935
The held-out test split, where available, is evaluated only after fine-tuning.
Multilingual smoke tests cover:
11 files, 2.3 MB in total. The weights are 1 file totalling 2.0 MB in safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 2.0 MB | ab39108b9aa6 |
| benchmark_results.json | Configuration | 2.4 KB | — |
| config.json | Configuration | 1.0 KB | — |
| generation_config.json | Configuration | 127 B | — |
| special_tokens_map.json | Configuration | 98 B | — |
| training_config.json | Configuration | 933 B | — |
| training_history.json | Configuration | 3.5 KB | — |
| README.md | Documentation | 1.0 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 264.3 KB | — |
| tokenizer_config.json | Tokenizer | 454 B | — |
Released by Kshitij Vinayaksing Rajput through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 2.0 MB |
| 16-bit | 0.0 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
About 0 GB at 16-bit and 0 GB at 4-bit: the weights (1M parameters) plus a working margin. A long context needs more.
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.
Yes. Quantum-Classifier-Calibrated 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.
128 tokens, from the maximum position embeddings in its published configuration.
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