On-Policy Distillation Meets Off-Policy GRPO: Training Compact Instruction-Following Rerankers
Paper • 2609.01947 • Published
How to use everetteonhf/zerank-2-distilled-1B-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M
docker model run hf.co/everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M
How to use everetteonhf/zerank-2-distilled-1B-GGUF with Ollama:
ollama run hf.co/everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M
How to use everetteonhf/zerank-2-distilled-1B-GGUF with Docker Model Runner:
docker model run hf.co/everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M
How to use everetteonhf/zerank-2-distilled-1B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull everetteonhf/zerank-2-distilled-1B-GGUF:Q4_K_M
lemonade run user.zerank-2-distilled-1B-GGUF-Q4_K_M
lemonade list
Static quants of anonymousauthor01/instruction_following_reranker (Prabhakar et al.) for use with llama.cpp.
Support for this model was added in PR #24083.
| File | Format | Size |
|---|---|---|
zerank-2-distilled-1B.bf16.gguf |
BF16 | 2.31 GiB |
zerank-2-distilled-1B.Q6_K.gguf |
Q6_K | 974 MiB |
zerank-2-distilled-1B.Q4_K_M.gguf |
Q4_K_M | 770 MiB |
Original model card:
Model Checkpoint
If you use this model, please cite:
@inproceedings{prabhakar2026onpolicy,
title = {On-Policy Distillation Meets Off-Policy {GRPO}:
Training Compact Instruction-Following Rerankers},
author = {Prabhakar, Vignesh and Pan, Jialing and
Ankisettipalli, Anil Babu},
booktitle = {Findings of the Association for Computational Linguistics:
EMNLP 2026},
year = {2026},
address = {Budapest, Hungary},
publisher = {Association for Computational Linguistics},
note = {To appear}
}
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