hw1-hc3-detector is an open-weight model from Yuchen Wu, released under Apache License 2.0. It has 23M parameters and a 512-token context. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Baseline accuracy: 0.8449; Fine-tuned test accuracy: 0.9869.
Runs On
What it takes to serve hw1-hc3-detector (23M 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 | 0.1 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.
hw1-hc3-detector on every accelerator the SAVRN Index prices, at every precision
Model Card
By Yuchen Wu, published under apache-2.0, revision e7e5ee4d5efa.
Baseline accuracy: 0.8449; Fine-tuned test accuracy: 0.9869.
Read Yuchen Wu's full model card
Baseline accuracy: 0.8449
Fine-tuned test accuracy: 0.9869
Configuration
- Architecture
- BertForSequenceClassification
- Context length (tokens)
- 512
- Layers
- 6
- Hidden size
- 384
- Feed-forward size
- 1,536
- Attention heads
- 12
- Vocabulary size
- 30,522
- Model type
- bert
Identity and Version
- Repository
- wuyuchen/hw1-hc3-detector
- Publisher
- Yuchen Wu
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 23M parameters
- Languages
- Not stated by the source
- Revision
- e7e5ee4d5efaeb220d955cde0d8d03a3e0f4e7f8
- First published
- 2026-09-24
- Last updated
- 2026-09-24
Files and Weights
6 files, 91.6 MB in total. The weights are 1 file totalling 90.9 MB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 90.9 MB | 8a6587ac1d51 |
| config.json | Configuration | 814 B | — |
| README.md | Documentation | 192 B | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 711.5 KB | — |
| tokenizer_config.json | Tokenizer | 594 B | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 90.9 MB
Released by Yuchen Wu through its official repository on Hugging Face. Read the license.
Built From
- Derived from sentence-transformers/all-MiniLM-L6-v2
- Trained on (disclosed) Hello-SimpleAI/HC3
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 90.9 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.
Questions About hw1-hc3-detector
How much GPU memory does hw1-hc3-detector need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (23M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run hw1-hc3-detector 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 hw1-hc3-detector commercially?
Yes. hw1-hc3-detector 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 hw1-hc3-detector's context length?
512 tokens, from the maximum position embeddings in its published configuration.