The Jina Reranker Tiny EN is a compact BERT-based model designed for re-ranking text results, enhancing the relevance of search outputs by refining the order of documents or snippets. With just 0.033 billion parameters, this model is exceptionally lightweight, making it highly efficient for devices with limited computational resources. It excels in scenarios where quick, on-the-fly re-ranking is needed without the overhead of larger, more resource-intensive models. The model supports a context length of 8192 tokens, which is quite generous for its size, allowing it to handle longer texts effectively.
Despite its small size, the Jina Reranker Tiny EN performs well, often delivering results that are comparable to larger models in terms of accuracy and relevance. This makes it an excellent choice for applications where efficiency and speed are critical, such as mobile devices, edge computing, or any environment with strict memory constraints. The model’s low VRAM requirement of 0.1 GB means it can run smoothly on a wide range of hardware, from older laptops to modern smartphones. Developers and researchers looking to integrate a lightweight yet powerful re-ranking solution into their projects will find this model particularly useful.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| FP16 | 16 | 0.067 GB | 0.15 GB | 0.3 GB | 85% |
How to run Jina Reranker Tiny EN
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Python — same API as BERT/MiniLM models.
Sentence-Transformers home →- 1
Install
pip install sentence-transformers - 2
Run
from sentence_transformers import SentenceTransformer m = SentenceTransformer("jinaai/jina-reranker-v1-tiny-en") v = m.encode(["hello world"])
Community benchmarks
Real tokens/sec reports from people running Jina Reranker Tiny EN on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
how much VRAM do I need to run Jina Reranker Tiny EN?
Jina Reranker Tiny EN requires 0.15 GB VRAM minimum with FP16 quantization. For full precision you need 0.15 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Jina Reranker Tiny EN?
Jina Reranker Tiny EN requires very little VRAM, so any GPU with at least 0.1 GB of VRAM will suffice. It can also run efficiently on CPUs.
Is Jina Reranker Tiny EN good for coding?
While Jina Reranker Tiny EN is primarily designed for improving search relevance, it can be used in coding contexts to enhance code search or documentation retrieval.
Jina Reranker Tiny EN vs Llama 3.1 8B?
Jina Reranker Tiny EN is much smaller (67MB) and requires less VRAM (0.1 GB), making it more suitable for resource-constrained environments. Llama 3.1 8B is larger and more powerful but requires significantly more resources.
Can I run Jina Reranker Tiny EN on a Mac?
Yes, you can run Jina Reranker Tiny EN on a Mac. It supports both CPU and GPU acceleration on macOS.
How much VRAM does Jina Reranker Tiny EN need?
Jina Reranker Tiny EN requires only 0.1 GB of VRAM, making it suitable for low-end GPUs or even running on CPUs.
Is Jina Reranker Tiny EN censored?
No, Jina Reranker Tiny EN is not censored. However, it is designed to improve search relevance and may filter out irrelevant or low-quality results.
Is Jina Reranker Tiny EN commercial-use allowed?
Yes, Jina Reranker Tiny EN is licensed under Apache-2.0, which allows commercial use without restrictions.
Jina Reranker Tiny EN context length?
Jina Reranker Tiny EN has a context length of 8192 tokens, allowing it to handle longer documents and queries.
Does Jina Reranker Tiny EN support function calling?
Jina Reranker Tiny EN does not support function calling as it is primarily a reranking model designed to improve search results.
Jina Reranker Tiny EN quantization options?
Jina Reranker Tiny EN supports quantization, which can reduce its size and improve inference speed. Common quantization options include INT8 and FP16.
Can Jina Reranker Tiny EN run on CPU?
Yes, Jina Reranker Tiny EN can run efficiently on CPU, making it suitable for devices without dedicated GPUs.
Jina Reranker Tiny EN fine-tuning?
Jina Reranker Tiny EN can be fine-tuned on your specific dataset to improve its performance for your particular use case.
Jina Reranker Tiny EN system requirements?
Jina Reranker Tiny EN requires minimal system resources. It can run on any device with at least 0.1 GB of VRAM and a few hundred MB of RAM.
Jina Reranker Tiny EN performance benchmark?
Jina Reranker Tiny EN processes approximately 500 tokens per second on a mid-range CPU and up to 1000 tokens per second on a low-end GPU.
Jina Reranker Tiny EN for RAG?
Jina Reranker Tiny EN can be used in Retrieval-Augmented Generation (RAG) pipelines to improve the relevance of retrieved documents.
Jina Reranker Tiny EN for agents?
Jina Reranker Tiny EN can be integrated into conversational agents to enhance the quality of search results and improve user interactions.
Jina Reranker Tiny EN for coding vs general?
Jina Reranker Tiny EN is versatile and can be used for both coding and general search tasks, but it excels in improving the relevance of search results in any context.
Jina Reranker Tiny EN vs ChatGPT?
Jina Reranker Tiny EN is much smaller and more lightweight compared to ChatGPT. While ChatGPT is a large language model capable of generating text, Jina Reranker Tiny EN focuses on improving search relevance.
Jina Reranker Tiny EN download size?
The download size of Jina Reranker Tiny EN is approximately 67MB, making it easy to deploy on devices with limited storage.
Best quant for Jina Reranker Tiny EN?
The best quantization option for Jina Reranker Tiny EN depends on your specific needs. INT8 provides a good balance between size reduction and performance, while FP16 offers a slight performance boost with minimal size increase.