amlnn-model-playground/examples/LLMs

LLM Examples

Resource Requirements

Model	CPU	NPU	GPU
Qwen(0.5B)	Minimum cores: 4 DDR: 4G (2G reserved for NN)	At least 3.2T	NO
Qwen(1.8B)	Minimum cores: 4 DDR: 8G (6G~6.5G reserved for NN)	At least 3.2T	NO
Gemma(2B)	Minimum cores: 4 DDR: 8G (5.5G~6G reserved for NN)	At least 3.2T	NO

Performance

ADLA2: A311D2_3.2T / S905X5_4T

LLM Model	SOC	Dtype	Seqlen	Max_Context	New_Tokens	TTFT(ms)	Tokens/s	memory(G)
DeepSeek-R1	A311D2	w8a8	64	320	256	927.79	4.95	1.99
DeepSeek-R1	S905X5	w8a8	64	320	256	514.86	4.47	1.73
Gemma-2B	A311D2	w8a8	64	320	256	846.66	2.64	3.93
Gemma-2B	S905X5	w8a8	64	320	256	482.92	3.08	2.77
Gemma-3-1B	A311D2	w8a8	64	320	256	702.88	5.08	1.9
Gemma-3-1B	S905X5	w8a8	64	320	256	468.97	6.44	1.38
Llama3.2_1B	A311D2	w8a8	64	320	256	711.64	5.92	1.69
Llama3.2_1B	S905X5	w8a8	64	320	256	695.92	5.42	1.5
Qwen1.5_1.8B	A311D2	w8a8	64	320	256	794.50	4.52	2.2
Qwen1.5_1.8B	S905X5	w8a8	64	320	256	983.93	4.47	1.9
Qwen2.5_0.5B	A311D2	w8a8	64	320	256	400.44	10.50	0.88
Qwen2.5_0.5B	S905X5	w8a8	64	320	256	400.37	10.97	0.66
Qwen2.5_1.5B	A311D2	w8a8	64	320	256	882.49	3.94	2.37
Qwen2.5_1.5B	S905X5	w8a8	64	320	256	874.06	4.16	1.76
TinyLlama-1.1B-Chat-v1.0	A311D2	w8a8	64	320	256	763.07	6.51	1.31
TinyLlama-1.1B-Chat-v1.0	S905X5	w8a8	64	320	256	1161.82	5.85	1.15
TinyLlama-1.1B-Chat-v0.4	A311D2	w8a8	64	320	256	740.02	6.38	1.31
TinyLlama-1.1B-Chat-v0.4	S905X5	w8a8	64	320	256	733.01	6.28	1.11

Download Models

Pre-quantized ADLA models are available on Hugging Face:

• Qwen2.5-0.5B (A311D2): Hugging Face Repository

Run LLM on Amlogic Devices

CPP

To compile the CPP project using Android NDK, please follow these steps:

1. Get the llmsdk library and header files: Clone the amlnn-toolkit repository to get the necessary libraries for compilation.

   # Clone to the parent directory of amlnn-model-playground
   git clone https://github.com/Amlogic-NN/amlnn-toolkit.git
   

2. Set the NDK path:

   export NDK_PATH=/your/ndk/path/android-ndk-r25c
   

3. Add NDK to your PATH:

   export PATH=$NDK_PATH:$PATH
   

4. Compile: Navigate to the cpp directory and run build-android.sh:

   cd examples/LLMs/cpp
   ./build-android.sh
   

5. Run: Push the compiled executable, model, and tokenizer to your Android device.

   Optional configuration:

   • Push llmsdk.so: If not already present on the device, push it to /data/local/tmp.
   • Set permissions:

     chmod +x demo_llm_main
     

   • Set environment variable:

     export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/vendor/lib64/:/data/local/tmp
     

   Then execute:

   ./demo_llm_main Qwen2.5-0.5B-Instruct_quant_i8_a311d2.adla tokenizer.json
   

Python (Arm-based Ubuntu)

Hardware Requirements:

• SOC: A311D2
• DDR: = 4GB

System Requirements:

• OS: Ubuntu 22.04

Caution

The system image is awaiting release; there is currently no official image available.

• Python: 3.10

Verify NPU Driver Version: Execute the following commands in the serial console to check the NPU driver version:

dmesg | grep adla
strings /usr/lib/libadla.so | grep LIBADLA

The driver version must be 1.7.x or higher.

1. Create Python Environment:

   # Install Miniforge if needed
   wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh
   bash Miniforge3-Linux-aarch64.sh
   
   # Create Environment
   conda create -n nnserver_310 python=3.10 -y
   conda activate nnserver_310
   

2. Get and install amlllm python whl: Clone the amlnn-toolkit repository to get the necessary libraries for compilation.

   # Clone to the parent directory of amlnn-model-playground
   git clone https://github.com/Amlogic-NN/amlnn-toolkit.git ../../../amlnn-toolkit
   
   # Install python whl
   pip install ../../../amlnn-toolkit/amlnn_edge_toolkit_lite/whl/amlllm-1.0.0-cp310-cp310-linux_aarch64.whl
   

3. Run: Navigate to thepydirectory and runsimple_chat.py:

   cd examples/LLMs/py
   python simple_chat.py --model <model_path> --tokenizer <tokenizer_path> [options]
   

4. Parameters:

   • --model: (Required) Path to LLM model file
   • --tokenizer: (Required) Path to tokenizer resources
   • --sampling-mode: Sampling mode, options: argmax, top_p, top_k, default: argmax
   • --top-k: Top-K parameter, default: 3
   • --top-p: Top-P parameter, default: 0.9
   • --temperature: Softmax temperature parameter, default: 1.0
   • --repeat-penalty: Repeat penalty factor, default: 1.1
   • --loglevel: Log level, options: DEBUG, INFO, WARNING, ERROR, default: ERROR
   • --model-type: Model type template, options: none, qwen, deepseek, gemma, gemma3, llama, tiny_llama, tiny_llama_v0_4, phi_1_5, phi_2, default: none

5. Usage Examples:

   # Using Qwen model
   python simple_chat.py --model Qwen2.5-0.5B-Instruct_quant_i8_a311d2.adla --tokenizer tokenizer.json --model-type qwen
   
   # Using Top-P sampling mode
   python simple_chat.py --model model.adla --tokenizer tokenizer.json --sampling-mode top_p --top-p 0.9 --temperature 0.8
   
   # Using Top-K sampling mode
   python simple_chat.py --model model.adla --tokenizer tokenizer.json --sampling-mode top_k --top-k 5
   

6. Interactive Commands: After the program starts, you enter an interactive interface that supports the following commands:

   • Direct input: Enter text and press Enter, the model will generate a response (streaming output)
   • exit: Exit the program
   • new_talk: Clear conversation history and start a new conversation
   • break: Interrupt the currently generating response
   • Ctrl+C: Send interrupt signal

Result