cwenzi/neuroflow-cpp

NeuroFlow C++ LLM

NeuroFlow is a memory-augmented causal language model implemented entirely in C++17, featuring a three-brain architecture (SN/ECN/DMN) inspired by cognitive neuroscience. Designed for efficient training and inference on consumer GPUs.

Model Architecture

Three-Brain Architecture

• —SN (Sensory Network): Input encoding and feature extraction
• —ECN (Executive Control Network): Core reasoning and processing layers
• —DMN (Default Mode Network): Memory-augmented association and retrieval

Files

Source Code

The full C++ source is included under src/ and include/ directories:

• —Core: tensor.hpp/cpp, model.hpp/cpp, tokenizer.hpp/cpp
• —Architecture: causal_lm.hpp/cpp, generative_model.hpp/cpp, networks.hpp
• —Training: train_lm.hpp/cpp, train_v2.cpp, sft_train.cpp, dpo_train.cpp
• —CUDA: cuda_context.hpp/cpp, cuda_kernels.hpp, tensor_ops.cpp
• —Optimizers: adamw.hpp/cpp, scheduler.hpp/cpp, grad_scaler.hpp/cpp

Build & Train

Prerequisites

• —CMake ≥ 3.15
• —C++17 compiler (GCC ≥ 9, MSVC 2019+)
• —CUDA Toolkit ≥ 11.4 (optional, for GPU training)
• —BLAS (OpenBLAS recommended)

Build

# CPU only
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)

# With CUDA
mkdir build_cuda && cd build_cuda
cmake .. -DNEUROFLOW_USE_CUDA=ON -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)

Train

./build_cuda/neuroflow_train_v2 \
  --config configs/config_distill.json \
  --data data/distill_train.txt \
  --output output \
  --epochs 20 \
  --batch-size 64 \
  --lr 0.0003 \
  --use-cuda --adam

Training Scripts

Key Python scripts in scripts/:

• —train_distill.py — Knowledge distillation training pipeline
• —preprocess_distill.py — Data preprocessing for distillation
• —deploy_dsw.sh — One-click deployment for Alibaba Cloud DSW (A10 GPU)
• —train_optimized.sh — Optimized multi-stage training

License

Apache 2.0

Links

• —GitHub Repository
• —HuggingFace Mirror