vlt5-base-keywords
> Our vlT5 model is a keyword generation model based on encoder-decoder architecture using Transformer blocks presented by Google (https://huggingface.co/t5-base). The vlT5 was trained on scientific articles corpus to predict a given set of keyphrases based on the concatenation of the article’s abstract and title. It generates precise, yet not always complete keyphrases that describe the content of the article based...
Params
280 M
Context
—
Downloads 30d
353 K
Likes
55
Download history
daily snapshots · 55 days
▲ 124 K in the last 30 days (54.0%)
353 K229 K
Aug 22Sep 1Sep 11Sep 20
353 K182 K
Jul 28Aug 15Sep 2Sep 20
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 1.1 GB | 1.8 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ curl -s https://aimodelscomparison.com/api/v1/models/vlt5-base-keywords
{
"hf_id": "Voicelab/vlt5-base-keywords",
"params_b": 0.28,
"context_length": null,
"license": { "id": "cc-by-4.0", "commercial": "yes" },
"downloads_30d": 353151,
"vram_estimates": [
{ "quant": "f32", "gb": 1.8 }
],
"updated_at": "2026-07-28T18:08:29Z"
}
Specifications
- Architecture
- T5ForConditionalGeneration
- Parameters
- 280 M
- Tensor type
- F32
- Vocabulary
- 50,048
- Licence
- cc-by-4.0
- First seen on the Hub
- 2022-09-27
- Training datasets
- posmac
- Added to our catalog
- 2026-07-28
Compare with any text-generation model