Menlo_Jan-nano-GGUF Open-source AI Model - Multiple quantization types meet the requirements of different hardware performance

Menlo Jan Nano GGUF

Developed by bartowski

Quantize the Menlo/Jan - nano model based on llama.cpp, providing model files of various quantization types to meet different hardware and performance requirements.

Large Language Model Open Source License:Apache-2.0 #Multiple quantization options #Low memory optimization #ARM/AVX acceleration

Downloads 190

Release Time : 6/15/2025

Model Overview

This project provides multiple quantized versions of the Menlo/Jan - nano model, suitable for different hardware configurations and usage scenarios, and supports running in tools such as LM Studio or llama.cpp.

Model Features

Multiple quantization types

Provide model files of various quantization types such as bf16, Q8_0, Q6_K_L, etc. Users can choose according to hardware and performance requirements.

Optimized weight processing

Some quantized models use the standard quantization method of quantizing embedding and output weights to Q8_0 to improve performance.

Online repackaging function

Support online repackaging of weights for some quantized models to improve performance on ARM and AVX machines.

Model Capabilities

Text generation

Multilingual support

Low - memory operation

Use Cases

Natural language processing

Dialogue system

Can be used to build a dialogue system, supporting multi - turn conversations.

Text generation

Suitable for various text generation tasks, such as article writing, code generation, etc.

🚀 Llamacpp imatrix Quantizations of Jan-nano by Menlo

This project offers quantized versions of the Menlo/Jan-nano model using llama.cpp, providing various quantization types for different performance and quality requirements.

🚀 Quick Start

Using llama.cpp release b5627 for quantization. The original model can be found at https://huggingface.co/Menlo/Jan-nano.

You can run these quantized models in LM Studio or directly with llama.cpp, or any other llama.cpp based project.

✨ Features

Multiple Quantization Types: Offers a wide range of quantization types, such as bf16, Q8_0, Q6_K_L, etc., to meet different performance and quality needs.
Online Repacking: Some quantization types support online repacking of weights, which can improve performance on ARM and AVX machines.
Download Flexibility: Allows downloading specific files or multiple split files using the huggingface-cli.

📦 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/Menlo_Jan-nano-GGUF --include "Menlo_Jan-nano-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/Menlo_Jan-nano-GGUF --include "Menlo_Jan-nano-Q8_0/*" --local-dir ./

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

💻 Usage Examples

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>

</think>

📚 Documentation

Download a file (not the whole branch) from below:

Property	Details
Filename	Jan-nano-bf16.gguf, Jan-nano-Q8_0.gguf, etc.
Quant type	bf16, Q8_0, Q6_K_L, Q6_K, etc.
File Size	8.05GB, 4.28GB, 3.40GB, etc.
Split	false
Description	Full BF16 weights, Extremely high quality, generally unneeded but max available quant, etc.

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.

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.

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.

🔧 Technical Details

Previously, weights of Q4_0_4_4/4_8/8_8 were interleaved in memory to improve performance on ARM and AVX machines. Now, "online repacking" for weights is available, which can automatically repack weights on the fly if using Q4_0 and the hardware benefits from it.

Benchmark on an AVX2 system (EPYC7702)

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

📄 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

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