We offer a suite of prompt-decoders, albeit with focus on open source LLMs compatible with FastChat (e.g., Vicuna, Zephyr, etc.). Some of the code in this repository is borrowed from RankGPT!
current_version = 0.2.8
conda create -n rankllm python=3.10
conda activate rankllmpip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118pip install -r requirements.txtpython 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_passagesIf you would like to contribute to the project, please refer to the contribution guidelines.
The following is a table of our 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 |
If you use RankLLM, please cite the following relevant papers:
@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}
}
This research is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent