Update README.md

README.md

@@ -6,8 +6,8 @@ tags: []
 # 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
 
@@ -19,151 +19,48 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 - **Developed by:** CIIR, UMass Amherst
 - **Model type:** Retriever
-- **Language(s) (NLP):** English
+- **Language(s):** English
 - **License:** MIT
-- **Finetuned from model [optional]:** Qwen-2.5-14B-Instruct
+- **Finetuned from model:** Qwen-2.5-14B-Instruct
 
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** https://github.com/Debrup-61/RaDeR
-- **Paper [optional]:** https://huggingface.co/papers/2505.18405
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-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
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-### Recommendations
+- **Paper** https://huggingface.co/papers/2505.18405
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
-Use the code below 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_Qwen25-14B_NuminaMath_MATH_allquerytypes \
+  --task embed \
+  --trust-remote-code \
+  --override-pooler-config '{"pooling_type": "LAST", "normalize": true}' \
+  --gpu-memory-utilization 0.9 \
+  --api-key abc \
+  --tokenizer Qwen/Qwen2.5-14B-Instruct \
+  --port 8001 \
+  --disable-log-requests \
+  --max-num-seqs 5000
+```
 
-[More Information Needed]
+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 [NuminaMath+MATH](https://huggingface.co/datasets/Raderspace/MATH_NuminaMath_allquerytypes) retrieval training dataset from RaDeR, containing all query types.  
 
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
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-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
-
-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
-
-[More Information Needed]
 
 #### Software
-
-[More Information Needed]
+https://github.com/Debrup-61/RaDeR
 
 ## Citation [optional]
 
@@ -171,26 +68,17 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **BibTeX:**
 
-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+@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}, 
+}
 
-[More Information Needed]
 
 ## Model Card Contact
 
-[More Information Needed]
+Debrup Das: debrupdas@umass.edu