Instructions to use pooria/foxmind-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use pooria/foxmind-small with Transformers.js:
// ⚠️ Unknown pipeline tag
- GLiNER2
How to use pooria/foxmind-small with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("pooria/foxmind-small") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
foxmind-small
foxmind-small is the small on-device model for the foxpilot apps (PII Guard, Consent Shield and others). It is an ONNX export of GLiNER2.5 small.
Base model: fastino/gliner2.5-small-v1 (Apache-2.0, 74M parameters, English). It runs on-device in Firefox with ONNX Runtime Web.
The export keeps foxpilot's four inputs (input_ids, attention_mask, word_positions, schema_positions). It returns cls_logits [1, L] for classification and span_probs [1, L, C] with span_bounds [1, C, 2] for span extraction over the boundary candidate pool. reference.json holds the library's own results on 14 calls.
| File | Size | Check against the library (14 calls) |
|---|---|---|
onnx/model.onnx (fp32) |
290 MB | 14/14 decisions, worst probability gap 0.0000 |
onnx/model_fp16.onnx |
147 MB | 14/14, worst gap 0.0005 |
onnx/model_quantized.onnx (q8) |
86 MB | 13/14, worst gap 0.1515 |
Firefox 157, wasm q8, one thread: 32 ms at 10 tokens, 120 ms at 64 tokens, 230 ms at 128 tokens.
Export script: export/export_gliner25.py in pooriaarab/foxpilot (MIT). Base model by Fastino, Apache-2.0.
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Model tree for pooria/foxmind-small
Base model
fastino/gliner2.5-small-v1