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
careconnect-indobert
by Dafa Ghani kangdap/careconnect-indobert
careconnect-indobert is an open-weight model for text classification from Dafa Ghani, released under MIT License. It has 124M parameters and a 512-token context. At 16-bit it needs about 0.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Base model: indobenchmark/indobert-base-p1; Input: title, description; Tokenization: maxlength=256, truncation=True; Output: Single label, with confidence score for each label..
Parameters124M
Context512
Weights497.8 MB
Licensemit
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve careconnect-indobert (124M 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 | 0.2 GB | 0.3 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 | 0.1 GB | 0.1 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.
careconnect-indobert on every accelerator the SAVRN Index prices, at every precision
Model Card
By Dafa Ghani, published under mit, revision c66ca0fca6c1.
Base model: indobenchmark/indobert-base-p1; Input: title, description; Tokenization: maxlength=256, truncation=True; Output: Single label, with confidence score for each label..
Read Dafa Ghani's full model card
Base model: indobenchmark/indobert-base-p1
Input: title, description
Tokenization: max_length=256, truncation=True
Output: Single label, with confidence score for each label.
Configuration
- Architecture
- BertForSequenceClassification
- Context length (tokens)
- 512
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 50,000
- Model type
- bert
Identity and Version
- Repository
- kangdap/careconnect-indobert
- Publisher
- Dafa Ghani
- Task
- Text classification
- Modality
- Text
- Library
- Not stated by the source
- Parameters
- 124M parameters
- Languages
- id
- Revision
- c66ca0fca6c18b33eaf32cd0bf99abf1b154c408
- First published
- 2026-09-27
- Last updated
- 2026-09-27
Files and Weights
6 files, 498.5 MB in total. The weights are 1 file totalling 497.8 MB in safetensors.
Weights1 file · 497.8 MB
Configuration1 file · 1.6 KB
Tokenizer2 files · 709.5 KB
Documentation1 file · 303 B
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 497.8 MB | 105fd930de7a |
| config.json | Configuration | 1.6 KB | — |
| README.md | Documentation | 303 B | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 709.1 KB | — |
| tokenizer_config.json | Tokenizer | 394 B | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 497.8 MB
Download from Dafa Ghani
Released by Dafa Ghani through its official repository on Hugging Face. Read the license.
Built From
- Derived from indobenchmark/indobert-base-p1
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 497.8 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About careconnect-indobert
How much GPU memory does careconnect-indobert need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (124M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run careconnect-indobert 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 careconnect-indobert commercially?
Yes. careconnect-indobert 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 careconnect-indobert's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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