Training models with ternary quantized weights using PyTorch
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Updated
Jun 12, 2019 - Python
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ternary-quantization
Here are 4 public repositories matching this topic...
Modular AI system where independently-trained ternary specialists load on demand from disk, routed by geometric classification, sharing a unified lattice knowledge base, capable of autonomously growing new specialists — all running on a CPU laptop.
golden-ratio language-model semantic-search rust-python bitnet no-gpu ternary-quantization coxeter-groups autonomous-research efficient-transformers cpu-training retrieval-augmented-generation tinystories e8-lattice geometric-attention h4-polytope 600-cell logarithmic-attention voronoi-indexing chamber-tree
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Mar 25, 2026 - Python
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
transformers pytorch parquet knowledge-distillation model-compression bitnet colab-notebook mixed-precision ternary-quantization large-language-model efficient-llm efficient-llm-inference activation-quantization kd-traces
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Sep 5, 2025 - Python
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.
research pytorch transformer language-model model-compression bitnet weight-sharing low-rank ternary-quantization quantization-aware-training efficient-deep-learning consumer-gpu
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Mar 20, 2026 - Python
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