cs-file-uploads
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domain-specific-adapter

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 ---
 base_model: google/gemma-3-27b-it
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:google/gemma-3-27b-it
 - lora
-- transformers
 ---
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-- PEFT 0.18.0

 ---
 base_model: google/gemma-3-27b-it
 library_name: peft
+license: gemma
 pipeline_tag: text-generation
 tags:
 - lora
+- peft
+- gemma3
+- information-extraction
 ---
+# Domain-Specific IE Adapter — Gemma 3 27B (long instruction)
+LoRA adapter for `google/gemma-3-27b-it` fine-tuned to extract compensation-consultant mentions from SEC proxy statements (DEF 14A), classifying each firm as:
+- **RET** — consultant retained/engaged as a compensation advisor
+- **SURV** — survey-only data provider (not retained as an advisor)
+Companion artifact for the anonymous submission *"From Lengthy Narrative to Structured Data: Instruction Fine-Tuning Open-Weight LLMs for Information Extraction from Corporate Disclosures."*
+## This adapter
+| | |
+|---|---|
+| Base model | `google/gemma-3-27b-it` |
+| Method | LoRA (r=8, α=16), 4-bit QLoRA |
+| Instruction format | **detailed (long)** |
+| Instance-level F1 | **95.9%** |
+Each adapter is trained for one instruction variant — pair this adapter with the **long** prompt at inference.
+## Adapter family (same task, 2,001-sample training set)
+| Adapter | Base | Instruction | F1 |
+|---|---|---|---|
+| `domain-specific-adapter` | Gemma 3 27B | detailed (long) | 95.9% |
+| `domain-specific-adapter-short` | Gemma 3 27B | minimal (short) | 96.1% |
+| `domain-specific-12b-adapter` | Gemma 3 12B | detailed (long) | 95.7% |
+| `domain-specific-12b-adapter-short` | Gemma 3 12B | minimal (short) | 93.0% |
+Evaluated on 316 consultants across 143 company-years from 84 SEC filings.
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+base = "google/gemma-3-27b-it"
+tok = AutoTokenizer.from_pretrained(base)
+model = AutoModelForCausalLM.from_pretrained(base, device_map="auto", load_in_4bit=True)
+model = PeftModel.from_pretrained(model, "cs-file-uploads/domain-specific-adapter")
+```
+See the code repository for the full inference pipeline (retrieval → chunking → extraction → grounding validation → cross-chunk aggregation) and the exact prompt templates.
+## Output format
+```
+{RET: 'Pearl Meyer & Partners, LLC'}, {SURV: 'Mercer', 'Radford'}
+```
+## Training
+2,001 human-labeled and augmented proxy-statement excerpts; LR 2e-4 (cosine, 3% warmup); max sequence length 5,120; 3 epochs; 20% validation split.
+## License
+Derived from Google **Gemma 3**; use is subject to the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). Adapter weights are released for research use.
+## Citation
+```bibtex
+@misc{anonymous2026fromlengthy,
+  title={From Lengthy Narrative to Structured Data: Instruction Fine-Tuning Open-Weight LLMs for Information Extraction from Corporate Disclosures},
+  author={Anonymous},
+  year={2026},
+  note={Under review}
+}
+```