Project Maya

edge0 is an open-source streaming Mixture-of-Experts inference framework for running sparse MoE models on Apple Silicon Macs. Its MLX backend memory-maps expert weights from SSD storage and loads them on demand, so peak memory depends on the active experts rather than the model's total parameter count. A trained prerouter predicts the next expert routes so storage reads can overlap the forward pass, while Recover-LoRA adapters distill from the full-precision model to compensate for quantization loss; the base weights remain read-only and the adapters stay separate. The framework ships two end-to-end model tiers with bundled 4-bit checkpoints, LoRA adapters, and prerouter heads. It provides a command-line interface and Python API, supports demo, chat, and server modes, and exposes an OpenAI-compatible `/v1/chat/completions` endpoint. The current backend supports macOS on Apple Silicon M1 through M4; CUDA support is identified as planned. The repository is licensed under Apache-2.0.

MiniMind is an open-source tiny large-language-model project and end-to-end training tutorial developed by Jingyao Gong. Its main dense model is approximately 64M parameters, and the repository provides the model architecture, tokenizer, datasets, inference code, and training pipeline. The pipeline covers pretraining, supervised fine-tuning, hand-written LoRA, DPO, PPO, GRPO, CISPO, model distillation, tool calling, adaptive thinking, and agentic reinforcement learning. Core algorithms are implemented directly with native PyTorch rather than relying on high-level abstractions from third-party training libraries. Its agentic reinforcement-learning path performs multi-turn rollouts, executes generated tool calls, appends tool observations to the context, calculates trajectory-level rewards, and updates the policy; rollout can use local PyTorch generation or an SGLang server. MiniMind supports dense and mixture-of-experts variants, single- and multi-GPU training, YaRN-based RoPE length extrapolation, Transformers-format models, and inference through llama.cpp, vLLM, or Ollama. The repository also includes a Streamlit chat interface and a lightweight OpenAI-compatible API server with tool-call and reasoning fields. It is released under the Apache License 2.0.

AirLLM is an open-source Python library for running large language model inference on GPUs with limited memory. It reduces GPU memory use by loading and processing one model layer at a time instead of holding the entire model in GPU memory; for sparse mixture-of-experts models, it streams individual experts as needed. The project is designed to run large models without requiring quantization, distillation, or pruning, and provides a pip-installable package with support for multiple model families, CPU inference, macOS, and optional model compression and quantization features.

mini-AGI is an experimental continual-learning, byte-level language model that trains from scratch on a CUDA-capable GPU with at least 8 GB of VRAM. It reads 256 byte values directly rather than using a conventional tokenizer, processes data one chunk at a time, and uses the same forward path for training and generation. Its architecture combines dense prelude blocks with a recurrent block applied up to 24 times, adaptive PonderNet-style halting, and per-application top-8 routing through a dynamically growing and pruning expert pool. Expert weights and Adam moments are stored as files on disk; a working set is paged through RAM and VRAM as needed, allowing the pool size to exceed available VRAM. The project includes Python commands for building corpora, reading files, streaming training, serving a local web interface, and inspecting or replicating continual-learning experiments. The repository describes the current model as toy-level rather than frontier-capable, and its weights are generated in the local weights directory rather than distributed with the source.

KTransformers is an open-source research framework for efficient large-language-model inference and fine-tuning through heterogeneous CPU-GPU computing. Its user-facing capabilities are provided through the kt-kernel source tree: high-performance inference and supervised fine-tuning (SFT). For mixture-of-experts models, it uses heterogeneous expert scheduling, placing frequently used or “hot” experts on GPUs and other experts on CPUs with optimized AMX, AVX512, or AVX2 kernels and NUMA-aware memory management. It supports CPU-side INT4/INT8 quantized weights, GPU-side GPTQ, native BF16 and FP8 precision, and multiple CPU, GPU, and NPU backends. The SFT capability integrates with LlamaFactory for hybrid CPU-GPU fine-tuning of large MoE models, including LoRA and full-parameter training, with INT4/INT8 quantization options.