Raderspace/RaDeR_Qwen_25_7B_instruct_MATH_LLMq_CoT_lexical

Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. --> RaDeR, are a set of reasoning-based dense retrieval and reranker models trained with data derived from mathematical problem solving using large language models (LLMs). RaDeR retrievers, trained for mathematical reasoning, effectively generalize to diverse retrieval reasoning tasks in the BRIGHT and RAR-b benchmarks, consistently outperforming strong baselines in overall performance.

Model Details

Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• —Developed by: CIIR, UMass Amherst
• —Model type: Retriever
• —Language(s): English
• —License: MIT
• —Finetuned from model: Qwen-2.5-7B-Instruct

Model Sources

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• —Repository: https://github.com/Debrup-61/RaDeR
• —Paper https://huggingface.co/papers/2505.18405

How to Get Started with the Model

Run the following code to start a server of the model with vLLM for fast inference.

vllm serve Raderspace/RaDeR_Qwen_25_7B_instruct_MATH_LLMq_CoT_lexical \
  --task embed \
  --trust-remote-code \
  --override-pooler-config '{"pooling_type": "LAST", "normalize": true}' \
  --gpu-memory-utilization 0.9 \
  --api-key abc \
  --tokenizer Qwen/Qwen2.5-7B-Instruct \
  --port 8001 \
  --disable-log-requests \
  --max-num-seqs 5000

Follow the code on Github to see how to query the retriever server.

Training Details

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> The model was trained using the MATH retrieval training dataset from RaDeR, containing CoT, LLMq and lexical query types.

Software

https://github.com/Debrup-61/RaDeR

Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

@misc{das2025raderreasoningawaredenseretrieval,
      title={RaDeR: Reasoning-aware Dense Retrieval Models}, 
      author={Debrup Das and Sam O' Nuallain and Razieh Rahimi},
      year={2025},
      eprint={2505.18405},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.18405}, 
}

Model Card Contact

Debrup Das: debrupdas@umass.edu