basemodel: - sentence-transformers/LaBSE - language-identification - langID - sentence-embedding - similarity-search - low-resource - low-resource-languages
Runs On
What it takes to serve LIBE (471M 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 | 0.9 GB | 1.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.5 GB | 0.6 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.2 GB | 0.3 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 18, 2026.
Model Card
basemodel: - sentence-transformers/LaBSE - language-identification - langID - sentence-embedding - similarity-search - low-resource - low-resource-languages
Excerpt from the card by Ca' Foscari NLP, licensed cc-by-nc-sa-4.0.
Configuration
- Architecture
- BertModel
- Context length (tokens)
- 512
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 501,153
- Stored precision
- float32
- Model type
- bert
Identity and Version
- Repository
- cafoscari-nlp/LIBE
- Publisher
- Ca' Foscari NLP
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 471M parameters
- Languages
- low-resource
- Revision
- 129e2a1de5beae561d293e268847a22d5548c629
- First published
- 2026-09-18
- Last updated
- 2026-09-18
Files and Weights
12 files, 1.9 GB in total. The weights are 1 file totalling 1.9 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 1.9 GB | de003d1b5e48 |
| 1_Pooling/config.json | Configuration | 296 B | — |
| config.json | Configuration | 860 B | — |
| config_sentence_transformers.json | Configuration | 201 B | — |
| modules.json | Configuration | 229 B | — |
| sentence_bert_config.json | Configuration | 53 B | — |
| special_tokens_map.json | Configuration | 695 B | — |
| README.md | Documentation | 225 B | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 13.6 MB | e160a07b3b76 |
| tokenizer_config.json | Tokenizer | 1.3 KB | — |
| vocab.txt | Tokenizer | 5.2 MB | — |
License and Download
- License
- cc-by-nc-sa-4.0
- Access
- Open weights, no gate
- Download size
- 1.9 GB
Released by Ca' Foscari NLP through its official repository on Hugging Face.
Built From
- Derived from sentence-transformers/LaBSE
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.9 GB |
| 16-bit | 0.9 GB |
| 8-bit | 0.5 GB |
| 4-bit | 0.2 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About LIBE
How much GPU memory does LIBE need?
About 1.1 GB at 16-bit and 0.3 GB at 4-bit: the weights (471M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run LIBE 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 LIBE commercially?
Not without separate permission. LIBE is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0. CC BY-NC-SA 4.0 permits non-commercial sharing and adapting with credit, and requires adaptations to use the same license. Commercial use needs separate permission.
What is LIBE's context length?
512 tokens, from the maximum position embeddings in its published configuration.