rank-llm 0.20.1

A Package for running prompt decoders like RankVicuna

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.) or vLLM. We also support RankGPT variants, which are proprietary listwise rerankers. Some of the code in this repository is borrowed from RankGPT, PyGaggle, and LiT5!

Releases

current_version = 0.20.1

📟 Instructions

Create Conda Environment

conda create -n rankllm python=3.10
conda activate rankllm

Install Pytorch with CUDA

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

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

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. 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.

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):

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}, 
}

🙏 Acknowledgments

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