BAAI_RoboBrain2.0-7B-GGUF Open-source Model - Multiple quantization types to adapt to different hardware requirements

BAAI RoboBrain2.0 7B GGUF

Developed by bartowski

This is the quantization version of BAAI's RoboBrain2.0-7B model, which is quantized through llama.cpp and provides various quantization types to meet different hardware requirements.

Large Language Model Open Source License:Apache-2.0 #Efficient quantization #Multi-hardware adaptation #Low memory optimization

Downloads 448

Release Time : 6/10/2025

Model Overview

This model is the quantization version of RoboBrain2.0-7B, aiming to improve the model's operating efficiency under different hardware conditions through specific quantization techniques.

Model Features

Multiple quantization types

It provides multiple quantization types from Q2_K to Q8_0, and users can choose the most suitable version according to their hardware conditions and performance requirements.

Efficient operation

The model is optimized through quantization techniques, enabling it to run efficiently on devices with limited resources.

Embedding and output weight optimization

Some quantized models perform special processing on embedding and output weights to improve model performance and quality.

Model Capabilities

Text generation

Multi-round dialogue

System prompt support

Use Cases

Natural language processing

Dialogue system

It can be used to build an intelligent dialogue system, supporting multi-round interactions.

Provide smooth and contextually relevant responses.

Content generation

Generate high-quality text content, such as articles and reports.

The generated content has high coherence and logic.

🚀 Llamacpp imatrix Quantizations of RoboBrain2.0-7B by BAAI

This project provides quantized versions of the BAAI/RoboBrain2.0-7B model using the llama.cpp library. It offers various quantization types to meet different performance and quality requirements.

✨ Features

Multiple Quantization Types: Offers a wide range of quantization types, such as bf16, Q8_0, Q6_K_L, etc., to balance between model quality and file size.
Easy to Use: Can be run in LM Studio or directly with llama.cpp.
Download Options: Supports downloading specific files or all split files using the huggingface-cli.

📦 Installation

Prerequisites

Make sure you have the huggingface-cli installed:

pip install -U "huggingface_hub[cli]"

Downloading a Specific File

huggingface-cli download bartowski/BAAI_RoboBrain2.0-7B-GGUF --include "BAAI_RoboBrain2.0-7B-Q4_K_M.gguf" --local-dir ./

Downloading Split Files

If the model is bigger than 50GB and split into multiple files, you can download them all to a local folder:

huggingface-cli download bartowski/BAAI_RoboBrain2.0-7B-GGUF --include "BAAI_RoboBrain2.0-7B-Q8_0/*" --local-dir ./

💻 Usage Examples

Prompt Format

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

📚 Documentation

Model Information

Property	Details
Quantized By	bartowski
Pipeline Tag	image-text-to-text
Base Model	BAAI/RoboBrain2.0-7B
Base Model Relation	quantized
License	apache-2.0

Downloadable Files

Filename	Quant type	File Size	Split	Description
RoboBrain2.0-7B-bf16.gguf	bf16	15.24GB	false	Full BF16 weights.
RoboBrain2.0-7B-Q8_0.gguf	Q8_0	8.10GB	false	Extremely high quality, generally unneeded but max available quant.
RoboBrain2.0-7B-Q6_K_L.gguf	Q6_K_L	6.52GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
RoboBrain2.0-7B-Q6_K.gguf	Q6_K	6.25GB	false	Very high quality, near perfect, recommended.
RoboBrain2.0-7B-Q5_K_L.gguf	Q5_K_L	5.78GB	false	Uses Q8_0 for embed and output weights. High quality, recommended.
RoboBrain2.0-7B-Q5_K_M.gguf	Q5_K_M	5.44GB	false	High quality, recommended.
RoboBrain2.0-7B-Q5_K_S.gguf	Q5_K_S	5.32GB	false	High quality, recommended.
RoboBrain2.0-7B-Q4_K_L.gguf	Q4_K_L	5.09GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
RoboBrain2.0-7B-Q4_1.gguf	Q4_1	4.87GB	false	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
RoboBrain2.0-7B-Q4_K_M.gguf	Q4_K_M	4.68GB	false	Good quality, default size for most use cases, recommended.
RoboBrain2.0-7B-Q3_K_XL.gguf	Q3_K_XL	4.57GB	false	Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
RoboBrain2.0-7B-Q4_K_S.gguf	Q4_K_S	4.46GB	false	Slightly lower quality with more space savings, recommended.
RoboBrain2.0-7B-Q4_0.gguf	Q4_0	4.44GB	false	Legacy format, offers online repacking for ARM and AVX CPU inference.
RoboBrain2.0-7B-IQ4_NL.gguf	IQ4_NL	4.44GB	false	Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
RoboBrain2.0-7B-IQ4_XS.gguf	IQ4_XS	4.22GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
RoboBrain2.0-7B-Q3_K_L.gguf	Q3_K_L	4.09GB	false	Lower quality but usable, good for low RAM availability.
RoboBrain2.0-7B-Q3_K_M.gguf	Q3_K_M	3.81GB	false	Low quality.
RoboBrain2.0-7B-IQ3_M.gguf	IQ3_M	3.57GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
RoboBrain2.0-7B-Q2_K_L.gguf	Q2_K_L	3.55GB	false	Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
RoboBrain2.0-7B-Q3_K_S.gguf	Q3_K_S	3.49GB	false	Low quality, not recommended.
RoboBrain2.0-7B-IQ3_XS.gguf	IQ3_XS	3.35GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
RoboBrain2.0-7B-IQ3_XXS.gguf	IQ3_XXS	3.11GB	false	Lower quality, new method with decent performance, comparable to Q3 quants.
RoboBrain2.0-7B-Q2_K.gguf	Q2_K	3.02GB	false	Very low quality but surprisingly usable.
RoboBrain2.0-7B-IQ2_M.gguf	IQ2_M	2.78GB	false	Relatively low quality, uses SOTA techniques to be surprisingly 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.

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 to 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

The quantizations are performed using llama.cpp release b5596. All quants are made using the imatrix option with a dataset from here.

📄 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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