llmlingua-2-bert-base-multilingual-cased-meetingbank
This model was introduced in the paper LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (Pan et al, 2024). It is a BERT multilingual base model (cased) finetuned to perform token classification for task agnostic prompt compression. The probability $p{preserve}$ of each token $xi$ is used as the metric for compression. This model is trained on the extractive text compression d...
Params
180 M
Context
512
Downloads 30d
905 K
Likes
65
Download history
daily snapshots · 106 days
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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 | 0.7 GB | 1.3 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- BertForTokenClassification
- Parameters
- 180 M
- Tensor type
- F32
- Context length
- 512
- Vocabulary
- 119,647
- Layers / heads
- 12 / 12
- Licence
- apache-2.0
- First seen on the Hub
- 2024-03-17
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
Compare with any token-classification model