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ChenYanKai2002
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Model Details
Model Provider
ChenYanKai2002
Model Tree
Qwen/Qwen3.6-27B
Input Modalities
Output Modalities
Supported Functionality
The combined adapter is available. This repository distributes one combined LoRA adapter, approximately 1.40 GB, to load on Qwen3.6-27B. It does not distribute the 27B base weights or the intermediate training adapters. Users load the base model and this adapter once.
The adapter consolidates all three training-stage LoRA updates by rank concatenation, with rank 48 and alpha 96. It preserves the original float32 adapter tensor blocks without rank truncation or SVD compression. composition.json records the artifact hash and numerical checks. This additive composition preserves the learned LoRA updates; sequential low-precision full-model merging can introduce different rounding. The exported combined adapter has not been separately re-benchmarked.
python
from transformers import Qwen3_5ForConditionalGenerationfrom peft import PeftModel base = Qwen3_5ForConditionalGeneration.from_pretrained( 'Qwen/Qwen3.6-27B', dtype='auto', device_map='auto')model = PeftModel.from_pretrained(base, 'ChenYanKai2002/OpenPerov-Flash')Use the OpenPerov GitHub repository for generation settings and evaluation code. A local copy of the recorded base model may be supplied instead of the upstream model ID.
OpenPerov Pro uses Flash as its answer backbone and adds evidence retrieval and local answer revision. Its source-retention selector (0.6B), scientific-relevance reranker (8B), and task-specific later-study evidence selector (8B) are available as separate final adapters in the OpenPerov collection.
The paper's frozen Flash deployment scored 87.5659 on the 800-question PSM-Bench. Questions, criteria, answers, score components and evaluation protocols are available in the accompanying code repository.
The adapter is released under Apache-2.0. The literature corpus, training examples, private retrieval index and evidence packets remain private. Upstream model licenses and notices are retained.