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qwen2.5-1.5b-invoice-lora
Msduck/qwen2.5-1.5b-invoice-lora
qwen2.5-1.5b-invoice-lora is a machine learning model from Msduck. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
- Base Model: Qwen/Qwen2.5-1.5B-Instruct - Adapter Model: Msduck/qwen2.5-1.5b-invoice-lora - Merged FP16 Model: Msduck/qwen2.5-1.5b-invoice-merged-fp16 - Quantized Model (AWQ 4-Bit): Msduck/qwen2.5-1.5b-invoice-awq -…
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Updated Aug 9, 2026
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At a glance
- Access
- Public
- Created
- Aug 8, 2026
- Updated
- Aug 9, 2026
- SHA
- 43171bb8
Model card
From the Hugging Face model README
Model Card — Invoice/PO Structured Extraction (Qwen2.5-1.5B-Instruct + LoRA)
Model Details
- Base Model:
Qwen/Qwen2.5-1.5B-Instruct - Adapter Model:
Msduck/qwen2.5-1.5b-invoice-lora - Merged FP16 Model:
Msduck/qwen2.5-1.5b-invoice-merged-fp16 - Quantized Model (AWQ 4-Bit):
Msduck/qwen2.5-1.5b-invoice-awq - Primary Task: Extract structured enterprise invoice and purchase order metadata into fixed JSON schema supporting English and Hindi documents.
Validation Numbers (Golden Test Set, n=135)
| Metric | Base Model | Fine-tuned Model | Threshold | Status |
|---|---|---|---|---|
| JSON Validity Rate | 88.15% | 100.0% | $\ge$ 95.0% | PASS |
| Field F1 Score | 0.7091 | 0.9951 | $\ge$ 0.90 | PASS |
| Field Precision | 0.7123 | 0.9951 | — | Diagnostic |
| Field Recall | 0.7074 | 0.9951 | — | Diagnostic |
| Field Exact Match | 0.7074 | 0.9951 | — | Diagnostic |
| Forgetting Retention | 45.82% | 49.82% | $\ge$ 85.0% | FAIL (Metric Discrepancy) |
Known Limitations / Edge Cases
- Exact-String Scoring: Field evaluation relies on lowercase exact string matching. Minor formatting differences (e.g.,
"1,000.00"vs"1000.0") are scored as mismatches unless sanitized downstream. - Catastrophic Forgetting Proxy: The retention score relies on n-gram overlap F1; human qualitative evaluation confirmed semantic retention is intact despite lower automated string-matching scores.
- Evaluation Sample Size: Golden test set contains 135 synthetic examples. Further testing on diverse real-world scanned documents is recommended before high-volume enterprise deployment.
- Created
- Aug 8, 2026
- Updated
- Aug 9, 2026