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---
library_name: transformers
tags:
- Reasoning
- Retrieval
license: mit
datasets:
- Raderspace/MATH_qCoT_LLMquery_lexicalquery
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
---
# 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
<!-- Provide a longer summary of what this model is. -->
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
<!-- Provide the basic links for the model. -->
- **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](https://github.com/Debrup-61/RaDeR/blob/main/models/RaDeR_retriever_server_API.py) 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](https://huggingface.co/datasets/Raderspace/MATH_qCoT_LLMquery_lexicalquery) 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: [email protected]
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