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engram-parser

Pure-Rust, zero-dependency GGUF deserializer and MoE per-expert weight extractor.

Version: 0.1.0 | Edition: 2024 | MSRV: 1.87 | License: MIT OR Apache-2.0

An engram is a physical trace of memory. This crate rips frozen weights (memories) out of MoE GGUF checkpoints so live systems can consume raw byte buffers with shape/dtype metadata — no neural-network math.

What It Does

• Parse GGUF v3 (magic, header, KV metadata, tensor directory) → GgufLayout
• Enumerate MoE experts: list_experts
• Extract one expert’s gate / up / down raw bytes: extract_expert
• Support stacked (ffn_*_exps.weight) and per-expert (ffn_*.E.weight) layouts

What It Does Not Do

• No matmul, forward, routing, softmax, or default-path dequant
• No CUDA / GPU / SIMD
• Zero crate dependencies ([dependencies] is empty)
• No model-family adapters or full checkpoint routing (see cortex-tensor)

Start Here

Page	Description
Getting Started	Install, load, extract
Project Structure	Modules and tree
Architecture	Ownership and non-goals
GGUF Parsing	load_gguf, layout, cursor
Tensor and DType	Directory entries and types
MoE Extraction	stacked vs per-expert
Errors	ParserError
Public API	Crate surface
Testing	Smoke tests
CI and Quality	GHA, security, Docker
MSRV	1.87 policy
Ecosystem	cortex-tensor, LIM-9
Glossary	Terms

Quick Commands

cargo fmt --check
cargo clippy --all-targets --all-features -- -D warnings
cargo build --all-features
cargo test --all-features

License

Dual-licensed under MIT or Apache-2.0 at your option.

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Last updated: July 29, 2026 Updated by: Grok Build: Grok 4.5 Package tip reference: 07a5558 (main, through PR #37)