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ymcki/
DeciLM-7B-Instruct-GGUF

Name: DeciLM-7B-Instruct-GGUF
Availability: InStock

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Original model: https://huggingface.co/Deci/DeciLM-7B-Instruct

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Modified llama.cpp to support DeciLMForCausalLM's variable Grouped Query Attention. Please download it and compile it to run the GGUFs in this repository.

Please note that the HF model of Deci-7B-Instruct uses dynamic NTK-ware RoPE scaling. However, llama.cpp doesn't support it yet, so my modifification also just ignore the dynamic NTK-ware RoPE scaling setting in the config.json. Since the ggufs seem working for the time being, please just use them as is until I figure out how to implement dynamic NTK-ware RoPE scaling.

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

Filename	Quant type	File Size	Description
DeciLM-7B-Instruct.f16.gguf	f16	14.1GB	Full F16 weights.
DeciLM-7B-Instruct.Q8_0.gguf	Q8_0	7.49GB	Extremely high quality, recommended.
DeciLM-7B-Instruct.Q4_K_M.gguf	Q4_K_M	4.24GB	Very good quality, recommended.
DeciLM-7B-Instruct.Q4_0.gguf	Q4_0	4GB	Good quality.
DeciLM-7B-Instruct.Q4_0_4_4.gguf	Q4_0_4_4	4GB	Good quality. recommended for edge devices <8GB RAM
DeciLM-7B-Instruct.Q4_0_4_8.gguf	Q4_0_4_8	4GB	Good quality. recommended for edge devices <8GB RAM
DeciLM-7B-Instruct.Q4_0_8_8.gguf	Q4_0_8_8	4GB	Good quality. recommended for edge devices <8GB RAM

How to check i8mm and sve support for ARM devices

ARM i8mm support is necessary to take advantage of Q4_0_4_8 gguf. All ARM architecture >= ARMv8.6-A supports i8mm.

ARM sve support is necessary to take advantage of Q4_0_8_8 gguf. sve is an optional feature that starts from ARMv8.2-A but majority of ARM chips doesn't implement it.

For ARM devices without both, it is recommended to use Q4_0_4_4.

With these support, the inference speed should be faster in the order of Q4_0_8_8 > Q4_0_4_8 > Q4_0_4_4 > Q4_0 without much effect on the quality of response.

This is a list of ARM CPUs that support different ARM instructions. Another list. Apparently, they only cover limited number of ARM CPUs. It is better you check for i8mm and sve support by yourself.

For Apple devices,

sysctl hw

For other ARM devices (ie most Android devices),

cat /proc/cpuinfo

There are also android apps that can display /proc/cpuinfo.

I was told that for Intel/AMD CPU inference, support for AVX2/AVX512 can also improve the performance of Q4_0_8_8.

On the other hand, Nvidia 3090 inference speed is significantly faster for Q4_0 than the other ggufs. That means for GPU inference, you better off using Q4_0.

Which Q4_0 model to use for ARM devices

Brand	Series	Model	i8mm	sve	Quant Type
Apple	A	A4 to A14	No	No	Q4_0_4_4
Apple	A	A15 to A18	Yes	No	Q4_0_4_8
Apple	M	M1	No	No	Q4_0_4_4
Apple	M	M2/M3/M4	Yes	No	Q4_0_4_8
Google	Tensor	G1,G2	No	No	Q4_0_4_4
Google	Tensor	G3,G4	Yes	Yes	Q4_0_8_8
Samsung	Exynos	2200,2400	Yes	Yes	Q4_0_8_8
Mediatek	Dimensity	9000,9000+	Yes	Yes	Q4_0_8_8
Mediatek	Dimensity	9300	Yes	No	Q4_0_4_8
Qualcomm	Snapdragon	7+ Gen 2,8/8+ Gen 1	Yes	Yes	Q4_0_8_8
Qualcomm	Snapdragon	8 Gen 2,8 Gen 3,X Elite	Yes	No	Q4_0_4_8

Convert safetensors to f16 gguf

Make sure you have llama.cpp git cloned:

python3 convert_hf_to_gguf.py DeciLM-7B-Instruct/ --outfile DeciLM-7B-Instruct.f16.gguf --outtype f16

Convert f16 gguf to Q8_0 gguf without imatrix

Make sure you have llama.cpp compiled:

./llama-quantize DeciLM-7B-Instruct.f16.gguf DeciLM-7B-Instruct.Q8_0.gguf q8_0

Downloading using huggingface-cli

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 ymcki/DeciLM-7B-Instruct-GGUF --include "DeciLM-7B-Instruct.Q8_0.gguf" --local-dir ./

Credits

Thank you bartowski for providing a README.md to get me started.