This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we…
This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package.
Parameters102M
Context512
Weights409.1 MB
License—
AccessOpen weights
Monthly Downloads54
Runs On
What it takes to serve posnegmodel (102M 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 | 0.2 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 Sep 18, 2026.
Model Card
This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we…
Excerpt from the card by Aidan Li.
Configuration
- Architecture
- BertModel
- Context length (tokens)
- 512
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 21,128
- Stored precision
- float32
- Model type
- bert
Identity and Version
- Repository
- aidanlli/posnegmodel
- Publisher
- Aidan Li
- Task
- Sentence similarity
- Modality
- Text
- Library
- sentence-transformers
- Parameters
- 102M parameters
- Languages
- zh
- Revision
- b92bf6b97e5aeae7e0ea8b1122de834db42c89d9
- First published
- 2024-07-10
- Last updated
- 2024-07-11
Files and Weights
16 files, 410.5 MB in total. The weights are 1 file totalling 409.1 MB in safetensors.
Weights1 file · 409.1 MB
Configuration9 files · 3.0 KB
Tokenizer3 files · 550.3 KB
Documentation1 file · 5.4 KB
Other1 file · 841.9 KB
Repository1 file · 1.7 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 409.1 MB | 568e6ab4e674 |
| 1_Pooling/config.json | Configuration | 270 B | — |
| LT_training_config.json | Configuration | 731 B | — |
| added_tokens.json | Configuration | 40 B | — |
| config.json | Configuration | 809 B | — |
| config_sentence_transformers.json | Configuration | 118 B | — |
| entity_vocab.json | Configuration | 62 B | — |
| modules.json | Configuration | 229 B | — |
| sentence_bert_config.json | Configuration | 53 B | — |
| special_tokens_map.json | Configuration | 695 B | — |
| README.md | Documentation | 5.4 KB | — |
| sentencepiece.bpe.model | Other | 841.9 KB | d8b73a5e0549 |
| .gitattributes | Repository | 1.7 KB | — |
| tokenizer.json | Tokenizer | 439.4 KB | — |
| tokenizer_config.json | Tokenizer | 1.4 KB | — |
| vocab.txt | Tokenizer | 109.5 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 409.1 MB
Download from Aidan Li
Released by Aidan Li through its official repository on Hugging Face.
Built From
- Described by arXiv:2309.00789
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 409.1 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 posnegmodel
How much GPU memory does posnegmodel need?
About 0.2 GB at 16-bit and 0.1 GB at 4-bit: the weights (102M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run posnegmodel 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.
What is posnegmodel's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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