GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
ChenYanKai2002
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
ChenYanKai2002
Model Tree
Qwen/Qwen3-Reranker-0.6B
Input Modalities
Output Modalities
Supported Functionality
Adapter weights are available in this repository. This repository contains a final LoRA adapter for Qwen/Qwen3-Reranker-0.6B, together with its configuration and tokenizer. Load this adapter directly onto the upstream base; an earlier training-initialization adapter is not an additional inference-time dependency. LoRA rank is 16, alpha is 32 and dropout is 0.05.
python
from transformers import AutoModelForCausalLMfrom peft import PeftModel base = AutoModelForCausalLM.from_pretrained('Qwen/Qwen3-Reranker-0.6B', device_map='auto')model = PeftModel.from_pretrained(base, 'ChenYanKai2002/OpenPerov-Pro-Source-Retention-Selector-0.6B')Replace HF_NAMESPACE with the owner of this repository. Use the ranking prompts and scoring code in the OpenPerov GitHub project. The relevance score is the next-token logit difference between yes and no.
OpenPerov Pro uses OpenPerov Flash as its answer backbone. Its source-retention selector establishes the article set; its scientific-relevance reranker orders that set; evidence assembly prepares the content used for local answer revision. The figure labels Knowledge extraction and Ranking Compression summarize evidence access and preparation; they are retained as in the manuscript.
On PSM-Bench, source retention at Top 40 was 728/800 (91.00%), an improvement of 8.25 percentage points over the preceding ranking.
The adapter and its configuration are released under Apache-2.0. The literature corpus, retrieval index, private evidence packets and training examples are not included. The public implementation accepts user-supplied evidence collections. Preserve the upstream base model's license and notices.