RekaAI_reka-flash-3.1-GGUF Open-source Model - Saves Resources and Is Suitable for Running on Constrained Devices

Rekaai Reka Flash 3.1 GGUF

Developed by bartowski

This is the Llamacpp imatrix quantized version of RekaAI's reka-flash-3.1 model. It reduces model storage space and computational resource requirements through quantization technology, making it suitable for running on resource-constrained devices.

Large Language Model Open Source License:Apache-2.0 #Efficient Quantization #Multi-hardware Adaptation #Low-resource Deployment

Downloads 1,449

Release Time : 7/10/2025

Model Overview

This project provides multiple quantized versions of the RekaAI reka-flash-3.1 model, aiming to reduce resource consumption while maintaining model performance, facilitating deployment and use on various hardware.

Model Features

Multiple Quantization Types

Provides multiple quantization options (such as bf16, Q8_0, Q6_K_L, etc.) to meet the balance requirements of different performance and quality.

Online Repackaging

Supports online repackaging technology, which can automatically optimize weights according to hardware conditions to improve model operation efficiency.

Detailed Selection Guide

Provides a detailed guide for selecting quantized files to help users choose the most suitable quantized version according to hardware resources.

Model Capabilities

Text Generation

Multilingual Support

Efficient Inference

Use Cases

Deployment on Resource-constrained Devices

Running on Edge Devices

Efficiently run large language models on edge devices with limited storage and computational resources.

Significantly reduce resource consumption while maintaining high model performance.

Research and Development

Model Quantization Research

Used to study and compare the impact of different quantization technologies on model performance.

Provide multiple quantized versions for comparative analysis.

🚀 Llamacpp imatrix Quantizations of reka-flash-3.1 by RekaAI

This project offers quantized versions of the reka-flash-3.1 model by RekaAI using llama.cpp. It provides various quantization types to meet different performance and quality requirements, and can be run in multiple environments.

Metadata

Property	Details
Quantized By	bartowski
Pipeline Tag	text-generation
Base Model	RekaAI/reka-flash-3.1
Base Model Relation	quantized
License	apache-2.0

🚀 Quick Start

Quantization Method

Using llama.cpp release b5856 for quantization.

Original Model

The original model can be found at: https://huggingface.co/RekaAI/reka-flash-3.1

Quantization Dataset

All quants are made using the imatrix option with the dataset from here

Running the Model

LM Studio: Run the quantized models in LM Studio
llama.cpp: Run them directly with llama.cpp, or any other llama.cpp based project

✨ Features

Prompt Format

human: {system_prompt} {prompt} <sep> assistant:

Downloadable Files

Filename	Quant type	File Size	Split	Description
reka-flash-3.1-bf16.gguf	bf16	41.82GB	false	Full BF16 weights.
reka-flash-3.1-Q8_0.gguf	Q8_0	22.22GB	false	Extremely high quality, generally unneeded but max available quant.
reka-flash-3.1-Q6_K_L.gguf	Q6_K_L	18.74GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
reka-flash-3.1-Q6_K.gguf	Q6_K	18.44GB	false	Very high quality, near perfect, recommended.
reka-flash-3.1-Q5_K_L.gguf	Q5_K_L	16.02GB	false	Uses Q8_0 for embed and output weights. High quality, recommended.
reka-flash-3.1-Q5_K_M.gguf	Q5_K_M	15.64GB	false	High quality, recommended.
reka-flash-3.1-Q5_K_S.gguf	Q5_K_S	14.79GB	false	High quality, recommended.
reka-flash-3.1-Q4_K_L.gguf	Q4_K_L	14.07GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
reka-flash-3.1-Q4_K_M.gguf	Q4_K_M	13.61GB	false	Good quality, default size for most use cases, recommended.
reka-flash-3.1-Q4_1.gguf	Q4_1	13.19GB	false	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
reka-flash-3.1-Q4_K_S.gguf	Q4_K_S	12.63GB	false	Slightly lower quality with more space savings, recommended.
reka-flash-3.1-Q4_0.gguf	Q4_0	11.96GB	false	Legacy format, offers online repacking for ARM and AVX CPU inference.
reka-flash-3.1-IQ4_NL.gguf	IQ4_NL	11.95GB	false	Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
reka-flash-3.1-Q3_K_XL.gguf	Q3_K_XL	11.95GB	false	Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
reka-flash-3.1-IQ4_XS.gguf	IQ4_XS	11.49GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
reka-flash-3.1-Q3_K_L.gguf	Q3_K_L	11.41GB	false	Lower quality but usable, good for low RAM availability.
reka-flash-3.1-Q3_K_M.gguf	Q3_K_M	10.86GB	false	Low quality.
reka-flash-3.1-IQ3_M.gguf	IQ3_M	10.26GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
reka-flash-3.1-Q3_K_S.gguf	Q3_K_S	9.93GB	false	Low quality, not recommended.
reka-flash-3.1-IQ3_XS.gguf	IQ3_XS	9.50GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
reka-flash-3.1-Q2_K_L.gguf	Q2_K_L	9.23GB	false	Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
reka-flash-3.1-IQ3_XXS.gguf	IQ3_XXS	9.18GB	false	Lower quality, new method with decent performance, comparable to Q3 quants.
reka-flash-3.1-Q2_K.gguf	Q2_K	8.63GB	false	Very low quality but surprisingly usable.
reka-flash-3.1-IQ2_M.gguf	IQ2_M	8.51GB	false	Relatively low quality, uses SOTA techniques to be surprisingly usable.
reka-flash-3.1-IQ2_S.gguf	IQ2_S	8.12GB	false	Low quality, uses SOTA techniques to be usable.

Embed/Output Weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

📦 Installation

Downloading using huggingface-cli

Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download bartowski/RekaAI_reka-flash-3.1-GGUF --include "RekaAI_reka-flash-3.1-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/RekaAI_reka-flash-3.1-GGUF --include "RekaAI_reka-flash-3.1-Q8_0/*" --local-dir ./

You can either specify a new local-dir (RekaAI_reka-flash-3.1-Q8_0) or download them all in place (./)

🔧 Technical Details

ARM/AVX information

Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.

Click to view Q4_0_X_X information (deprecated

I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.

Click to view benchmarks on an AVX2 system (EPYC7702)

model	size	params	backend	threads	test	t/s	% (vs Q4_0)
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	pp512	204.03 ± 1.03	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	pp1024	282.92 ± 0.19	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	pp2048	259.49 ± 0.44	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	tg128	39.12 ± 0.27	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	tg256	39.31 ± 0.69	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	tg512	40.52 ± 0.03	100%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	pp512	301.02 ± 1.74	147%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	pp1024	287.23 ± 0.20	101%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	pp2048	262.77 ± 1.81	101%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	tg128	18.80 ± 0.99	48%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	tg256	24.46 ± 3.04	83%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	tg512	36.32 ± 3.59	90%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	pp512	271.71 ± 3.53	133%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	pp1024	279.86 ± 45.63	100%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	pp2048	320.77 ± 5.00	124%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	tg128	43.51 ± 0.05	111%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	tg256	43.35 ± 0.09	110%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	tg512	42.60 ± 0.31	105%

Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation

📚 Documentation

Which file should I choose?

Click here for details

A great write up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

📄 License

This project is licensed under the apache-2.0 license.

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Thank you to LM Studio for sponsoring my work.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご