LLaMA-Mesh-GGUF Open-Source Model - Achieve Text-to-3D Mesh Conversion, Multiple Quantizations for Different Hardware

Llama Mesh GGUF

Developed by bartowski

LLaMA-Mesh is a text-to-3D model based on the LLaMA architecture, focusing on mesh generation tasks. This version is quantized using llama.cpp, offering multiple quantization options to suit different hardware requirements.

Text-to-Image #Text-to-3D #High-precision Mesh Generation #Multiple Quantization Versions

Downloads 1,911

Release Time : 11/20/2024

Model Overview

LLaMA-Mesh is a model that converts text descriptions into 3D meshes, suitable for the 3D modeling and design fields. This quantized version provides various precision options, enabling efficient operation across different hardware configurations.

Model Features

Multiple Quantization Options

Offers various quantization versions from f16 to Q2_K, catering to different hardware and performance needs.

High-Quality Quantization

Uses the imatrix option for quantization, ensuring high-quality output even after compression.

Hardware Optimization

Some quantized versions are specifically optimized for ARM and AVX inference, improving operational efficiency.

Model Capabilities

Text-to-3D Mesh Generation

Supports Multiple Quantization Precisions

Hardware-Accelerated Inference

Use Cases

3D Modeling

Rapid Prototyping

Quickly generates 3D meshes from text descriptions, accelerating the product prototyping process.

Produces mesh models ready for 3D printing or further editing

Game Development

Game Asset Generation

Automatically generates 3D objects and scene elements for games based on text descriptions.

Reduces time and cost in art asset production

🚀 Llamacpp imatrix Quantizations of LLaMA-Mesh

This project focuses on the Llama.cpp imatrix quantizations of the LLaMA-Mesh model. It leverages the llama.cpp framework for quantization, offering various quantized models for different usage scenarios.

Quantization Details

Quantization Tool: Using llama.cpp release b4132 for quantization.
Original Model: https://huggingface.co/Zhengyi/LLaMA-Mesh
Quantization Option: All quants are made using the imatrix option with a dataset from here.

Running the Model

You can run these quantized models in LM Studio.

✨ Features

Prompt Format

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Downloadable Files

Property	Details
Model Type	LLaMA-Mesh with various quantizations
Training Data	Dataset from here

Filename	Quant type	File Size	Split	Description
LLaMA-Mesh-f16.gguf	f16	16.07GB	false	Full F16 weights.
LLaMA-Mesh-Q8_0.gguf	Q8_0	8.54GB	false	Extremely high quality, generally unneeded but max available quant.
LLaMA-Mesh-Q6_K_L.gguf	Q6_K_L	6.85GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
LLaMA-Mesh-Q6_K.gguf	Q6_K	6.60GB	false	Very high quality, near perfect, recommended.
LLaMA-Mesh-Q5_K_L.gguf	Q5_K_L	6.06GB	false	Uses Q8_0 for embed and output weights. High quality, recommended.
LLaMA-Mesh-Q5_K_M.gguf	Q5_K_M	5.73GB	false	High quality, recommended.
LLaMA-Mesh-Q5_K_S.gguf	Q5_K_S	5.60GB	false	High quality, recommended.
LLaMA-Mesh-Q4_K_L.gguf	Q4_K_L	5.31GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
LLaMA-Mesh-Q4_K_M.gguf	Q4_K_M	4.92GB	false	Good quality, default size for most use cases, recommended.
LLaMA-Mesh-Q3_K_XL.gguf	Q3_K_XL	4.78GB	false	Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
LLaMA-Mesh-Q4_K_S.gguf	Q4_K_S	4.69GB	false	Slightly lower quality with more space savings, recommended.
LLaMA-Mesh-Q4_0.gguf	Q4_0	4.68GB	false	Legacy format, generally not worth using over similarly sized formats
LLaMA-Mesh-Q4_0_8_8.gguf	Q4_0_8_8	4.66GB	false	Optimized for ARM and AVX inference. Requires 'sve' support for ARM (see details below). Don't use on Mac.
LLaMA-Mesh-Q4_0_4_8.gguf	Q4_0_4_8	4.66GB	false	Optimized for ARM inference. Requires 'i8mm' support (see details below). Don't use on Mac.
LLaMA-Mesh-Q4_0_4_4.gguf	Q4_0_4_4	4.66GB	false	Optimized for ARM inference. Should work well on all ARM chips, not for use with GPUs. Don't use on Mac.
LLaMA-Mesh-IQ4_XS.gguf	IQ4_XS	4.45GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
LLaMA-Mesh-Q3_K_L.gguf	Q3_K_L	4.32GB	false	Lower quality but usable, good for low RAM availability.
LLaMA-Mesh-Q3_K_M.gguf	Q3_K_M	4.02GB	false	Low quality.
LLaMA-Mesh-IQ3_M.gguf	IQ3_M	3.78GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
LLaMA-Mesh-Q2_K_L.gguf	Q2_K_L	3.69GB	false	Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
LLaMA-Mesh-Q3_K_S.gguf	Q3_K_S	3.66GB	false	Low quality, not recommended.
LLaMA-Mesh-IQ3_XS.gguf	IQ3_XS	3.52GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
LLaMA-Mesh-Q2_K.gguf	Q2_K	3.18GB	false	Very low quality but surprisingly usable.
LLaMA-Mesh-IQ2_M.gguf	IQ2_M	2.95GB	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.

📦 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/LLaMA-Mesh-GGUF --include "LLaMA-Mesh-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/LLaMA-Mesh-GGUF --include "LLaMA-Mesh-Q8_0/*" --local-dir ./

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

🔧 Technical Details

Q4_0_X_X information

These are NOT for Metal (Apple) or GPU (nvidia/AMD/intel) offloading, only ARM chips (and certain AVX2/AVX512 CPUs).

If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons on the original pull request

To check which one would work best for your ARM chip, you can check AArch64 SoC features (thanks EloyOn!).

If you're using a CPU that supports AVX2 or AVX512 (typically server CPUs and AMD's latest Zen5 CPUs) and are not offloading to a GPU, the Q4_0_8_8 may offer a nice speed as well:

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 and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

📄 License

This project is licensed under the llama3.1 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.

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

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