appbinary-inspector

Static inspection tools plus an open-source, disk-paged character-model architecture.

Features

• Detects PE, ELF, Mach-O, ZIP, 7z, and RAR headers
• Calculates SHA-256 and SHA-1 hashes
• Extracts readable strings and entropy
• Reads basic PE header metadata
• Extracts embedded ZIP archives and carves recognizable binary signatures
• Exports PE headers and raw binary sections for authorized analysis
• Builds a compact tokenizer for source code, logs, or extracted text
• Provides editable PyTorch classifier, autoencoder, and transformer templates
• Keeps CardStack model cards as separately stored 4-bit SSD files
• Loads only router-selected cards into a small LRU cache during generation
• Generates one next byte at a time for true character-level output

Install

pip install appbinary-inspector

Use

appbin inspect program.exe
appbin extract program.exe --output extracted
appbin binary-extract program.exe --output pe_parts
appbin tokenize source.py --vocab source-tokenizer.json
appbin model-template classifier

CardStack: low-VRAM model experiments

CardStack is a small trainable byte-level language model. Its expert cards are stored as separate 4-bit files. At inference, inactive cards remain on disk and the cache retains only a limited number of recently selected cards.

from appbinary_inspector import CardStackConfig, CardStackLM, train_next_character

model = CardStackLM(CardStackConfig(width=128, layers=4, cards_per_layer=8, active_cards=2, cache_cards=4))
losses = train_next_character(model, open("training.txt", encoding="utf-8").read(), steps=500)
model.save("my-card-model")  # writes a base file plus separate 4-bit card files

paged = CardStackLM.load("my-card-model")
print(paged.generate("Hello", new_characters=80))

CLI version:

appbin card-create --output my-card-model --width 128 --layers 4 --cards 8 --active-cards 2
appbin card-train my-card-model training.txt --steps 500
appbin card-generate my-card-model "Hello" --characters 80

This reduces active model-card memory. It does not make a dense, unmodified 70B model fit instantly on a low-VRAM GPU: disk bandwidth and CPU/GPU transfer time become the tradeoff, and models need to be trained for sparse routing.

PyTorch models

Install optional PyTorch support:

pip install "appbinary-inspector[torch]"

from appbinary_inspector.torch_builder import create_model, preset

config = preset("classifier")
config["layers"][0]["out_features"] = 512
model = create_model(config)

Safety

Targets are read as bytes only and never executed. The package does not defeat encryption, DRM, licensing controls, passwords, or code protections. It can analyze plaintext, export raw PE sections, and extract recognizable embedded content from files you are authorized to inspect.