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 |
| Tensor | G1,G2 | No | No | Q4_0_4_4 | |
| 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.
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