### slide-bge-reranker-v2-m3-GGUF Open-Source Model - Free Deployment to Boost English Text Reordering Tasks

Slide Bge Reranker V2 M3 GGUF

Developed by mradermacher

This is the statically quantized version of the Menghuan1918/slide-bge-reranker-v2-m3 model, primarily used for English text reranking tasks.

Text Embedding EnglishOpen Source License:Apache-2.0 #English Reranking #Static Quantization #Efficient Inference

Downloads 302

Release Time : 2/5/2025

Model Overview

This model is a text reranking model based on the BGE architecture, quantized in GGUF format, suitable for efficient inference deployment.

Model Features

Multiple Quantized Versions

Offers multiple quantized versions from Q2_K to f16 to meet performance and accuracy needs in different scenarios.

Efficient Inference

After quantization, the model size is significantly reduced, and inference speed is improved, making it suitable for resource-constrained environments.

Recommended Quantized Versions

Q4_K_S and Q4_K_M versions are labeled as 'fast, recommended' and suitable for most application scenarios.

Model Capabilities

Text Reranking

Efficient Inference

Use Cases

Information Retrieval

Search Result Reranking

Rerank search engine results to improve the ranking of relevant documents.

Enhances the relevance and accuracy of search results

Recommendation Systems

🚀 Slide-BGE Reranker V2-M3 GGUF Quantized Model

This repository provides static quantizations of the Slide-BGE Reranker V2-M3 model, offering various quantization options for efficient usage.

📚 Documentation

About

This is a static quantization of the model from https://huggingface.co/Menghuan1918/slide-bge-reranker-v2-m3.

Weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ - quants are often preferable over similar sized non - IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q2_K	0.5
GGUF	Q3_K_S	0.5
GGUF	Q3_K_M	0.5	lower quality
GGUF	IQ4_XS	0.5
GGUF	Q3_K_L	0.5
GGUF	Q4_K_S	0.5	fast, recommended
GGUF	Q4_K_M	0.5	fast, recommended
GGUF	Q5_K_S	0.6
GGUF	Q5_K_M	0.6
GGUF	Q6_K	0.6	very good quality
GGUF	Q8_0	0.7	fast, best quality
GGUF	f16	1.3	16 bpw, overkill

Here is a handy graph by ikawrakow comparing some lower - quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

📄 License

This model is licensed under the apache - 2.0 license.

📦 Model Information

Property	Details
Base Model	Menghuan1918/slide-bge-reranker-v2-m3
Language	en
Library Name	transformers
Quantized By	mradermacher
Quantize Version	Not specified
Output Tensor Quantised	Not specified
Convert Type	hf
Vocab Type	Not specified
Tags	Not specified

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