This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Open-weight model · Text classification
UIUC-CS546-HW1-HC3-Detector
by EC XYgugugu/UIUC-CS546-HW1-HC3-Detector
UIUC-CS546-HW1-HC3-Detector is an open-weight model for text classification from EC, released under MIT License. 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.
Epochs: 5; Batch size: 32; Optimizer: AdamW; Max input token length: 256.
Parameters23M
Context512
Weights90.9 MB
Licensemit
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve UIUC-CS546-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.
UIUC-CS546-HW1-HC3-Detector on every accelerator the SAVRN Index prices, at every precision
Model Card
By EC, published under mit, revision 9dfd7933c3e4.
Epochs: 5; Batch size: 32; Optimizer: AdamW; Max input token length: 256.
Read EC's full model card
Training
Epochs: 5
Batch size: 32
Optimizer: AdamW
LR: 2e-5
Max input token length: 256
Results
| Model | Test accuracy |
|---|---|
| Logistic regression on frozen sentence embeddings | 0.8449 |
| Fine-tuned sequence classifier | 0.9925 |
Use
from transformers import pipeline
detector = pipeline(
"text-classification",
model="XYgugugu/UIUC-CS546-HW1-HC3-Detector",
)
print(detector("Text to classify goes here."))
Configuration
- Architecture
- BertForSequenceClassification
- Context length (tokens)
- 512
- Layers
- 6
- Hidden size
- 384
- Feed-forward size
- 1,536
- Attention heads
- 12
- Vocabulary size
- 30,522
- Stored precision
- float32
- Model type
- bert
Identity and Version
- Repository
- XYgugugu/UIUC-CS546-HW1-HC3-Detector
- Publisher
- EC
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- 23M parameters
- Languages
- en
- Revision
- 9dfd7933c3e4300cc3b3798d136e60428d2b8052
- First published
- 2026-10-01
- Last updated
- 2026-10-01
Files and Weights
8 files, 91.8 MB in total. The weights are 1 file totalling 90.9 MB in safetensors.
Weights1 file · 90.9 MB
Configuration2 files · 1.4 KB
Tokenizer3 files · 944.5 KB
Documentation1 file · 669 B
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 90.9 MB | 18535c93402c |
| config.json | Configuration | 712 B | — |
| special_tokens_map.json | Configuration | 732 B | — |
| README.md | Documentation | 669 B | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 711.5 KB | — |
| tokenizer_config.json | Tokenizer | 1.5 KB | — |
| vocab.txt | Tokenizer | 231.5 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 90.9 MB
Download from EC
Released by EC 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 UIUC-CS546-HW1-HC3-Detector
How much GPU memory does UIUC-CS546-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 UIUC-CS546-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 UIUC-CS546-HW1-HC3-Detector commercially?
Yes. UIUC-CS546-HW1-HC3-Detector is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
What is UIUC-CS546-HW1-HC3-Detector's context length?
512 tokens, from the maximum position embeddings in its published configuration.
Similar Models
Baseline test accuracy: 84.49%; Fine-tuned test accuracy: 99.21%.
This is my fine-tuned model for Homework 1. The model classifies HC3 answers as: - 0: human - 1: ChatGPT sentence-transformers/all-MiniLM-L6-v2 I used the fixed dataset splits provided in the notebook.
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).