Training models with ternary quantized weights using PyTorch
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Updated
Jun 12, 2019 - Python
Training models with ternary quantized weights using PyTorch
n0x CLI is my go-to Claude Code alternative when I’m on a budget.
Custom Apple Metal kernels for Hy4-preview (770B ternary MoE) in llama.cpp — makes AngelSlim's STQ1_0 GGUF runnable on Apple Silicon
Pre-quantized ternary models for VLMs, multimodal, and audio — the models GGUF can't touch
1.58-bit ternary Mamba LLM for Indian languages. Weights are {-1,0,+1} — inference uses only add/sub. 3B model fits in 750MB, runs 20+ tok/s on mobile.
Selective ternary compression of Qwen3.6 MoE expert projections on Apple Silicon, with matched-control functional, routing, and perplexity evaluation.
Research implementation of activation-aware ternary and mixed-bit quantization for Qwen3.8-27B, targeting 7-9 GB text inference on 16 GB GPUs.
Anonymous paper release for TRACE-Q, a topology-aware ternary quantization framework for heterogeneous language models.
Colab-friendly BitNet distillation engine: collect KD traces from a teacher, train a ternary Mini-BitNet, and dry-run 7B memory. Multi-provider + Drive/S3
PILON (Primitive-Induced Linear Operator Network) explores a compositional weight parameterization for transformer FFN layers. The goal is to replace dense FFN matrices with shared low-rank primitives plus learned composition weights.
ESP32 LLM / MoE: trained on Google Colab GPU, ternary-quantized, exported to C++, and verified on real ESP32 hardware. Fork of Ahmed Barakat's ESP-LLM.
First open-source qutrit (d=3) variational quantum classifier for tabular data. Lossless 2³→3² binary-to-ternary compression, CSUM ring entanglement, and SHAP explainability. 72 parameters. Built with PennyLane + PyTorch.
Independent forensics of Bonsai 2 27B ternary quantization: format and basis recovery, the trained-weights residual, and the public PTQ calibration artifact.
jade_mage · agentprivacy dual-agent harness lane for JadeZaher/ternary-memory-research (fork: main tracks upstream; lane on branch harness/jade_mage; contributions go upstream by PR)
Apple Silicon MLX low-bit ternary (BitNet b1.58 {-1,0,+1}) quantization, Metal GPU kernels, perplexity benchmarking, the cogito corpus pipeline
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