RankLLM

We offer a suite of rerankers - pointwise models like monoT5 and listwise models with a focus on open source LLMs compatible with FastChat (e.g., Vicuna, Zephyr, etc.), vLLM, SGLang, or TensorRT-LLM. We also support RankGPT variants, which are proprietary listwise rerankers. Addtionally, we support reranking with the first-token logits only to improve inference efficiency. Some of the code in this repository is borrowed from RankGPT, PyGaggle, and LiT5!

Releases

current_version = 0.20.3

Note for Mac Users: RankLLM is not compatible with Apple Silicon (M1/M2) chips. However, you can still run it by using the Intel-based version of Anaconda and launching your terminal through Rosetta 2.

📟 Instructions

Create Conda Environment

conda create -n rankllm python=3.10
conda activate rankllm

Install Pytorch with CUDA (Windows/Linux)

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Install Pytorch with MPS (Mac)

pip3 install torch torchvision torchaudio

Install openjdk with maven if you want to use the retriever

conda install -c conda-forge openjdk=21 maven -y

Install Dependencies

pip install -r requirements.txt

Install vLLM, SGLang, or TensorRT-LLM (Optional)

vLLM

pip install -e .[vllm]      # local installation for development
pip install rank-llm[vllm]  # or pip installation

SGLang

pip install -e .[sglang]      # local installation for development
pip install rank-llm[sglang]  # or pip installation

Remember to install flashinfer to use SGLang backend.

pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/

TensorRT-LLM

pip install -e .[tensorrt-llm]      # local installation for development
pip install rank-llm[tensorrt-llm]  # or pip installation

Run end to end - RankZephyr

We can run the RankZephyr model with the following command:

python src/rank_llm/scripts/run_rank_llm.py  --model_path=castorini/rank_zephyr_7b_v1_full --top_k_candidates=100 --dataset=dl20 \
--retrieval_method=SPLADE++_EnsembleDistil_ONNX --prompt_mode=rank_GPT  --context_size=4096 --variable_passages

Including the --vllm_batched flag will allow you to run the model in batched mode using the vLLM library.

Including the --sglang_batched flag will allow you to run the model in batched mode using the SGLang library.

Including the --tensorrt_batched flag will allow you to run the model in batched mode using the TensorRT-LLM library.

If you want to run multiple passes of the model, you can use the --num_passes flag.

Run end to end - RankGPT4-o

We can run the RankGPT4-o model with the following command:

python src/rank_llm/scripts/run_rank_llm.py  --model_path=gpt-4o --top_k_candidates=100 --dataset=dl20 \
  --retrieval_method=bm25 --prompt_mode=rank_GPT_APEER  --context_size=4096 --use_azure_openai

Note that the --prompt_mode is set to rank_GPT_APEER to use the LLM refined prompt from APEER. This can be changed to rank_GPT to use the original prompt.

Run end to end - LiT5

We can run the LiT5-Distill V2 model (which could rerank 100 documents in a single pass) with the following command:

python src/rank_llm/scripts/run_rank_llm.py  --model_path=castorini/LiT5-Distill-large-v2 --top_k_candidates=100 --dataset=dl19 \
    --retrieval_method=bm25 --prompt_mode=LiT5  --context_size=150 --vllm_batched --batch_size=4 \
    --variable_passages --window_size=100

We can run the LiT5-Distill original model (which works with a window size of 20) with the following command:

python src/rank_llm/scripts/run_rank_llm.py  --model_path=castorini/LiT5-Distill-large --top_k_candidates=100 --dataset=dl19 \
    --retrieval_method=bm25 --prompt_mode=LiT5  --context_size=150 --vllm_batched --batch_size=32 \
    --variable_passages

We can run the LiT5-Score model with the following command:

python src/rank_llm/scripts/run_rank_llm.py  --model_path=castorini/LiT5-Score-large --top_k_candidates=100 --dataset=dl19 \
    --retrieval_method=bm25 --prompt_mode=LiT5 --context_size=150 --vllm_batched --batch_size=8 \
    --window_size=100 --variable_passages

Run end to end - monoT5

The following runs the 3B variant of monoT5 trained for 10K steps:

python src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/monot5-3b-msmarco-10k --top_k_candidates=1000 --dataset=dl19 \
  --retrieval_method=bm25 --prompt_mode=monot5 --context_size=512

Note that we usually rerank 1K candidates with monoT5.

Run end to end - FirstMistral

We can run the FirstMistral model, reranking using the first-token logits only with the following command:

python src/rank_llm/scripts/run_rank_llm.py  --model_path=castorini/first_mistral --top_k_candidates=100 --dataset=dl20 --retrieval_method=SPLADE++_EnsembleDistil_ONNX --prompt_mode=rank_GPT  --context_size=4096 --variable_passages --use_logits --use_alpha --vllm_batched --num_gpus 1

Omit --use_logits if you wish to perform traditional listwise reranking.

If you would like to contribute to the project, please refer to the contribution guidelines.

🦙🐧 Model Zoo

The following is a table of the listwise models our repository was primarily built to handle (with the models hosted on HuggingFace):

vLLM, SGLang, and TensorRT-LLM backends are only supported for RankZephyr and RankVicuna models.

Model Name	Hugging Face Identifier/Link
RankZephyr 7B V1 - Full - BF16	castorini/rank_zephyr_7b_v1_full
RankVicuna 7B - V1	castorini/rank_vicuna_7b_v1
RankVicuna 7B - V1 - No Data Augmentation	castorini/rank_vicuna_7b_v1_noda
RankVicuna 7B - V1 - FP16	castorini/rank_vicuna_7b_v1_fp16
RankVicuna 7B - V1 - No Data Augmentation - FP16	castorini/rank_vicuna_7b_v1_noda_fp16

We also officially support the following rerankers built by our group:

LiT5 Suite

The following is a table specifically for our LiT5 suite of models hosted on HuggingFace:

Model Name	Hugging Face Identifier/Link
LiT5 Distill base	castorini/LiT5-Distill-base
LiT5 Distill large	castorini/LiT5-Distill-large
LiT5 Distill xl	castorini/LiT5-Distill-xl
LiT5 Distill base v2	castorini/LiT5-Distill-base-v2
LiT5 Distill large v2	castorini/LiT5-Distill-large-v2
LiT5 Distill xl v2	castorini/LiT5-Distill-xl-v2
LiT5 Score base	castorini/LiT5-Score-base
LiT5 Score large	castorini/LiT5-Score-large
LiT5 Score xl	castorini/LiT5-Score-xl

Now you can run top-100 reranking with the v2 model in a single pass while maintaining efficiency!

monoT5 Suite - Pointwise Rerankers

The following is a table specifically for our monoT5 suite of models hosted on HuggingFace:

Model Name	Hugging Face Identifier/Link
monoT5 Small MSMARCO 10K	castorini/monot5-small-msmarco-10k
monoT5 Small MSMARCO 100K	castorini/monot5-small-msmarco-100k
monoT5 Base MSMARCO	castorini/monot5-base-msmarco
monoT5 Base MSMARCO 10K	castorini/monot5-base-msmarco-10k
monoT5 Large MSMARCO 10K	castorini/monot5-large-msmarco-10k
monoT5 Large MSMARCO	castorini/monot5-large-msmarco
monoT5 3B MSMARCO 10K	castorini/monot5-3b-msmarco-10k
monoT5 3B MSMARCO	castorini/monot5-3b-msmarco
monoT5 Base Med MSMARCO	castorini/monot5-base-med-msmarco
monoT5 3B Med MSMARCO	castorini/monot5-3b-med-msmarco

We recommend the Med models for biomedical retrieval. We also provide both 10K (generally better OOD effectiveness) and 100K checkpoints (better in-domain).

✨ References

If you use RankLLM, please cite the following relevant papers:

[2309.15088] RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models

@ARTICLE{pradeep2023rankvicuna,
  title   = {{RankVicuna}: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models},
  author  = {Ronak Pradeep and Sahel Sharifymoghaddam and Jimmy Lin},
  year    = {2023},
  journal = {arXiv:2309.15088}
}

[2312.02724] RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!

@ARTICLE{pradeep2023rankzephyr,
  title   = {{RankZephyr}: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!},
  author  = {Ronak Pradeep and Sahel Sharifymoghaddam and Jimmy Lin},
  year    = {2023},
  journal = {arXiv:2312.02724}
}

If you use one of the LiT5 models please cite the following relevant paper:

[2312.16098] Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models

@ARTICLE{tamber2023scaling,
  title   = {Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models},
  author  = {Manveer Singh Tamber and Ronak Pradeep and Jimmy Lin},
  year    = {2023},
  journal = {arXiv:2312.16098}
}

If you use one of the monoT5 models please cite the following relevant paper:

[2101.05667] The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models

@ARTICLE{pradeep2021emd,
  title = {The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models},
  author = {Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin},
  year = {2021},
  journal = {arXiv:2101.05667},
}

If you use the FirstMistral model, please consider citing:

[2411.05508] An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking

@ARTICLE{chen2024firstrepro,
  title   = title={An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking},
  author  = {Zijian Chen and Ronak Pradeep and Jimmy Lin},
  year    = {2024},
  journal = {arXiv:2411.05508}
}

If you would like to cite the FIRST methodology, please consider citing:

[2406.15657] FIRST: Faster Improved Listwise Reranking with Single Token Decoding

@ARTICLE{reddy2024first,
  title   = {FIRST: Faster Improved Listwise Reranking with Single Token Decoding},
  author  = {Reddy, Revanth Gangi and Doo, JaeHyeok and Xu, Yifei and Sultan, Md Arafat and Swain, Deevya and Sil, Avirup and Ji, Heng},
  year    = {2024}
  journal = {arXiv:2406.15657},
}

🙏 Acknowledgments

This research is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.