codesoda/gliner2-onnx
AI model by codesoda
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Model card & documentation
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Explicit, opt-in model downloads for codesoda/gliner2-rs. Rust build and inference do not require Python. These are fp32, ONNX opset 17 conversions, not Fastino's original safetensors checkpoints. Each 2.5 directory includes seven graphs: encoder, classifier, boundary marginals, shared scorer, explicit scorer, records and relations. Config and tokenizer metadata, the original model card, Apache2 license and conversion notice are colocated. exportmanifest.json records required files, sizes, SHA-256 hashes, actual graph signatures, source identities and validation. validationreport.json contains measured checkpoint-specific source/ONNX/native evidence; exportmanifest.unvalidated.json preserves the pre-review inventory. The existing v2 graph files remain unchanged. The project downloaders also fetch v2 tokenizer/config metadata from the immutable original Fastino revisions above and colocate it with the downloaded graphs. Original GLiNER2 source: d7c727458bf6929bc9ef5ee04e13c3f717a7c455. The Rust runtime uses direct ort 2.0.0-rc.13 / native ONNX Runtime 1.28. Development Python validation uses separately pinned ONNX Runtime 1.20.1 and PyTorch 2.8.0; it is not a Rust runtime dependency. Each 2.5 checkpoint…
Read the full model card ↗ · Preview checked 2026-10-30T11:25:29.103Z
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Model overview
codesoda/gliner2-onnx is a task-unspecified model repository published by codesoda on Hugging Face. A library was not reported.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- Not reported
- Library
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- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
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- Reported safetensors parameters
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- Architecture
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- License
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- Access
- Not gated by Hugging Face
- Created
- 2026-09-18T06:09:42.000Z
- Last modified
- 2026-09-18T06:09:42.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-18T06:35:09.247Z. Daily imports are snapshots, not real-time monitoring.
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