Llama.cpp
The primary goal of llama.cpp is to enable LLM inference on various hardware (both local and cloud) with minimal setup and state-of-the-art performance.
Clone the Repository
git clone https://github.com/ggml-org/llama.cpp.git
Compile llama.cpp
Install Build Tools
sudo apt install cmake gcc g++
Build the Project
cmake -B build
cmake --build build --config Release
tip
If you are using the Radxa Orion O6 with an ARM-v9 CPU, you can add the armv9-a compile option for hardware-level optimization:
cmake -B build -DCMAKE_CXX_FLAGS="-march=armv9-a" -DCMAKE_C_FLAGS="-march=armv9-a"
cmake --build build --config Release
tip
Llama.cpp integrates Arm's KleidiAI library, which provides optimized matrix multiplication kernels for hardware features like sme, i8mm, and dot-product acceleration.
You can enable this feature using the GGML_CPU_KLEIDIAI build option:
cmake -B build -DGGML_CPU_KLEIDIAI=ON
cmake --build build --config Release
Usage
GGUF Model Conversion
tip
Here, we take DeepSeek-R1-Distill-Qwen-1.5B as an example.
Download the Hugging Face Model
Use git LFS to clone the repository:
git lfs install
git clone https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
Generate GGUF Model
tip
It is recommended to use Python 3.11 or later.
cd llama.cpp
pip3 install -r ./requirements.txt
python3 convert_hf_to_gguf.py DeepSeek-R1-Distill-Qwen-1.5B/
Quantize the Model
cd build/bin
./llama-quantize DeepSeek-R1-Distill-Qwen-1.5B-F16.gguf DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf Q4_K_M
Available quantization types:
2 or Q4_0 : 4.34G, +0.4685 ppl @ Llama-3-8B
3 or Q4_1 : 4.78G, +0.4511 ppl @ Llama-3-8B
8 or Q5_0 : 5.21G, +0.1316 ppl @ Llama-3-8B
9 or Q5_1 : 5.65G, +0.1062 ppl @ Llama-3-8B
19 or IQ2_XXS : 2.06 bpw quantization
20 or IQ2_XS : 2.31 bpw quantization
28 or IQ2_S : 2.5 bpw quantization
29 or IQ2_M : 2.7 bpw quantization
24 or IQ1_S : 1.56 bpw quantization
31 or IQ1_M : 1.75 bpw quantization
36 or TQ1_0 : 1.69 bpw ternarization
37 or TQ2_0 : 2.06 bpw ternarization
10 or Q2_K : 2.96G, +3.5199 ppl @ Llama-3-8B
21 or Q2_K_S : 2.96G, +3.1836 ppl @ Llama-3-8B
23 or IQ3_XXS : 3.06 bpw quantization
26 or IQ3_S : 3.44 bpw quantization
27 or IQ3_M : 3.66 bpw quantization mix
12 or Q3_K : alias for Q3_K_M
22 or IQ3_XS : 3.3 bpw quantization
11 or Q3_K_S : 3.41G, +1.6321 ppl @ Llama-3-8B
12 or Q3_K_M : 3.74G, +0.6569 ppl @ Llama-3-8B
13 or Q3_K_L : 4.03G, +0.5562 ppl @ Llama-3-8B
25 or IQ4_NL : 4.50 bpw non-linear quantization
30 or IQ4_XS : 4.25 bpw non-linear quantization
15 or Q4_K : alias for Q4_K_M
14 or Q4_K_S : 4.37G, +0.2689 ppl @ Llama-3-8B
15 or Q4_K_M : 4.58G, +0.1754 ppl @ Llama-3-8B
17 or Q5_K : alias for Q5_K_M
16 or Q5_K_S : 5.21G, +0.1049 ppl @ Llama-3-8B
17 or Q5_K_M : 5.33G, +0.0569 ppl @ Llama-3-8B
18 or Q6_K : 6.14G, +0.0217 ppl @ Llama-3-8B
7 or Q8_0 : 7.96G, +0.0026 ppl @ Llama-3-8B
1 or F16 : 14.00G, +0.0020 ppl @ Mistral-7B
32 or BF16 : 14.00G, -0.0050 ppl @ Mistral-7B
0 or F32 : 26.00G @ 7B
COPY : only copy tensors, no quantizing
Run GGUF Model
cd build/bin
./llama-cli -m DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf
> hi, who are you
<think>
</think>
Hi! I'm DeepSeek-R1, an artificial intelligence assistant created by DeepSeek. I'm at your service and would be delighted to assist you with any inquiries or tasks you may have.
GGUF Benchmark Test
./llama-bench -m DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf
radxa@orion-o6:~/llama.cpp/build/bin$ ./llama-bench -m ~/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf -t 8
| model | size | params | backend | threads | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
| qwen2 1.5B Q4_K - Medium | 1.04 GiB | 1.78 B | CPU | 8 | pp512 | 64.60 ± 0.27 |
| qwen2 1.5B Q4_K - Medium | 1.04 GiB | 1.78 B | CPU | 8 | tg128 | 36.29 ± 0.16 |
References
For more details on llama.cpp, please refer to the official documentation.