Meta LLaMA 4边缘设备部署：从模型量化到实际应用

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make -j

# 将模型转换为GGUF格式
python convert.py /path/to/llama-4-8b --outfile llama-4-8b.gguf

# 量化到Q4_K_M（推荐平衡方案）
./quantize llama-4-8b.gguf llama-4-8b-Q4_K_M.gguf q4_k_m

# 创建Modelfile
FROM ./llama-4-8b-Q4_K_M.gguf
TEMPLATE "{{ .Prompt }}"
PARAMETER temperature 0.7
PARAMETER num_ctx 4096

# 创建并运行
ollama create llama4-edge -f Modelfile
ollama run llama4-edge

# 在树莓派5上安装llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DCMAKE_C_FLAGS="-march=armv8.2-a+dotprod"
cmake --build build --config Release

# 运行LLaMA 4-8B Q4量化版（约5GB内存）
./build/bin/main -m llama-4-8b-Q4_K_M.gguf -p "你好" -n 128

import onnxruntime as ort
sess = ort.InferenceSession("llama4.onnx")
outputs = sess.run(None, {"input_ids": tokens})
print(tokenizer.decode(outputs[0]))

# 联邦学习配置
config = {
    "num_clients": 10,
    "rounds": 20,
    "fraction_fit": 0.5,
    "local_epochs": 3,
    "strategy": "fed_avg"
}
result = fl.run_federated_learning(config)