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+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# PVminerLLM2_3B
+
+PVminerLLM2 is a set of Large Language Models (LLMs) optimized for the structured extraction of patient voice information from unstructured text. This model specifically represents the 3B parameter variant.
+
+The model was introduced in the paper [PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization](https://huggingface.co/papers/2606.16074).
+
+## Model Description
+
+PVminerLLM2 improves upon prior work in structured patient voice extraction by applying preference optimization to address token-critical errors that supervised fine-tuning (SFT) often misses. These errors are frequently rare, fine-grained, and unevenly distributed in structured outputs.
+
+Key methodological improvements include:
+- **Token-level Gated Stabilization:** A preference objective term that prevents the degradation of absolute token likelihood.
+- **Confusion-aware Preference Pair Construction:** A strategy to better capture low-separation distinctions between different extraction categories.
+- **Reweighing Strategies:** Incorporation of token-importance weighting and inverse-frequency reweighing to address token imbalance and class skew.
+
+Across various model sizes, PVminerLLM2 consistently outperforms standard baselines and models trained with existing preference optimization methods.
+
+## Links
+
+- **Paper:** [PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization](https://huggingface.co/papers/2606.16074)
+- **GitHub Repository:** [Data-Mining-Lab-Yale/PVminerLLM2](https://github.com/Data-Mining-Lab-Yale/PVminerLLM2)
+
+## Implementation Details
+
+The official repository provides a full training and evaluation pipeline, including scripts for generating confusion summaries, building preference data, training with LoRA adapters, and merging models. For detailed implementation instructions, please refer to the [GitHub README](https://github.com/Data-Mining-Lab-Yale/PVminerLLM2).