qriton-hlm 0.7.2

Energy Language — program neural networks by shaping energy landscapes

Qriton HLM — Energy Language

Program neural networks by shaping energy landscapes.

The first tool that lets you directly program a neural network's behavior — not train, not fine-tune, not prompt-engineer. Program.

"Energy minima as a programming language — in a completely new fashion." — John J. Hopfield, March 2026

$ qriton-hlm -c model.pt

hlm:model> capture 5 polite Thank you so much for your help
  Captured L5: "Thank you so much for your help"
  Energy: -12.34 | Basin: True (cos=0.97, 23 iters)
  -> Added to concept 'polite' (1 samples)

hlm:model> capture 5 polite I really appreciate your patience
  -> Added to concept 'polite' (2 samples)

hlm:model> inject-concept 5 polite 0.1
  Before: 200 basins, concept is basin: False
  After:  201 basins (+1), concept is basin: True
  >> Concept successfully injected!

hlm:model> apply 5
hlm:model> generate Tell me about the weather
  I'd be happy to share! The weather today is...

Install

pip install qriton-hlm

What is this?

Every AI framework today has one way to change model behavior: training. Qriton HLM adds a second: surgery.

This only works because HLM uses polynomial Hopfield dynamics that create multiple stable basins per layer. Transformers can't do this — their softmax attention collapses to a single attractor.

32 Operations — Complete Energy Language

Observe (read the landscape)

Command	What it does
survey <layer>	Map all basins in a layer
survey-all	Survey every layer
verify <layer> <seed>	Check if a point is a basin
energy <layer> <seed>	Measure energy at a point
probe <layer> [basin_idx]	What tokens does a basin activate? (reverse capture)
landscape <layer>	Full energy map with population bars

Modify (change the landscape)

Command	What it does
inject <layer> <seed> [str]	Create a new attractor
remove <layer> <seed> [str]	Destroy an attractor
move <layer> <seed> [str]	Relocate an attractor
strengthen <layer> <seed> [f]	Deepen an existing basin
weaken <layer> <seed> [f]	Make a basin shallower

Concept (semantic operations)

Command	What it does
capture <layer> <concept> <text>	Extract what a concept looks like in the model
inject-concept <layer> <concept> [s]	Program a captured concept as a new attractor
remove-concept <layer> <concept> [s]	Remove a concept from the model
blend <a> <b> <new> [ratio]	Mix two concepts (e.g. 70% polite + 30% formal)
concepts	List all captured concepts
export-concept <name> <path>	Save concept as portable file
import-concept <path>	Load concept from file

Control (flow & persistence)

Command	What it does
load <path>	Load a checkpoint
apply <layer>	Write modified W to live model
restore <layer>	Undo modifications
restore-all	Restore all layers
save <path>	Save modified W matrices
status	Show which layers are modified
set <param> <value>	Set parameter (beta, strength, ...)
info	Show model info

Verify (test & audit)

Command	What it does
generate <prompt>	Generate text with current model
diff <layer>	Show W matrix change stats
benchmark	Measure perplexity impact of surgery
guard <layer> [max_%]	Prevent removing too many basins
history	Show operation log

Scripts

Write .hlm scripts to chain operations:

# make_polite.hlm
load model.pt
capture 5 polite Thank you so much
capture 5 polite I really appreciate it
capture 5 polite That's very kind of you
inject-concept 5 polite 0.1
apply 5
benchmark
generate Tell me about the weather
restore 5

Run with: qriton-hlm --script make_polite.hlm

Python API

from qriton_hlm import BasinSurgeon

surgeon = BasinSurgeon.from_checkpoint("model.pt", device="cuda")

# Capture what a concept looks like in the model
surgeon.capture(layer=5, text="Thank you so much", concept_name="polite")
surgeon.capture(layer=5, text="I really appreciate it", concept_name="polite")

# Inject that concept as a new attractor
result = surgeon.inject_concept(layer=5, concept_name="polite", strength=0.1)
print(f"Concept injected: {result['exists_after']}")

# Blend two concepts
surgeon.blend("polite", "formal", "professional", ratio=0.6)

# Export concept for sharing
surgeon.export_concept("polite", "polite.concept")

# Transplant concept from another model
other = BasinSurgeon.from_checkpoint("other_model.pt")
surgeon.transplant(other, layer=5, concept_name="humor")

# Apply to live model and benchmark
surgeon.apply(layer=5)
result = surgeon.benchmark()
print(f"PPL after surgery: {result['perplexity']:.2f}")

# Probe: what does basin #3 represent?
probe = surgeon.probe(layer=5, basin_idx=3)
print(f"Top tokens: {probe['top_tokens']}")

# Compare basins between models
diff = surgeon.compare(other, layer=5)
print(f"Shared: {diff['shared']}, unique: {diff['only_self']}")

Compatibility

Works with any PyTorch checkpoint that contains hopfield.W parameters:

• HLM2/HLM3 language models
• HLM-Spatial (LIDAR, Medical3D, Industrial3D)
• HLM-Audio (STT, TTS)
• Any model using PolyHopfieldLayer

Requirements

• Python >= 3.9
• PyTorch >= 2.0
• NumPy

License

Apache 2.0 — Qriton Technologies S.R.L.