gliner_large-v2
GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
Params
460 M
Context
—
Downloads 30d
240 K
Likes
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Download history
daily snapshots · 16 days262 K201 K
Oct 26Oct 31Nov 5Nov 10
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 | 1.8 GB | 2.6 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Parameters
- 460 M
- Tensor type
- F32
- Licence
- apache-2.0
- First seen on the Hub
- 2024-03-10
- Training datasets
- numind/NuNER
- Added to our catalog
- 2026-10-26
Compare with any token-classification model