breed-registry 1.0.1

The Breed Registry — model selection as breeding selection, from Working Animal Architecture

🐕 The Breed Registry

Model selection as breeding selection. In the working dog paradigm, choosing the right breed is the most important decision you'll make. This registry provides structured guidance on which base model to use for which task.

Why a Breed Registry?

A working dog isn't a pet — it's bred for a purpose. Border collies herd. Labs retrieve. German shepherds protect. Each breed has instincts, temperaments, and physical traits honed over generations of selective breeding.

Foundation models are the same. GPT-4 is a generalist — like a German shepherd that can do everything. Llama-3 is an open-lineage working dog you can train yourself. Mistral is a lean, efficient breed from European lines.

Choosing the wrong breed for the job wastes resources and produces poor results. This registry exists to prevent that.

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What's Inside

registry/ — Breed Assessments

Each model is assessed like a dog breed at a confirmation show, but for working aptitude instead of conformation:

Field	Dog Analogy	Model Meaning
lineage	Pedigree / bloodline	Model family and training lineage
breed_group	AKC group (Herding, Sporting, etc.)	Task category (General Purpose, Working, etc.)
temperament	Breed temperament traits	Behavioral characteristics (careful, verbose, etc.)
working_aptitude	Instinct tests & working trials	Task-specific capability scores (0-10)
cost_profile	Feed & maintenance cost	API cost per token
speed_profile	Speed/agility rating	Inference latency
trainability	How well it takes to training	Fine-tune response quality
recommended_for	Best-suited jobs	Tasks where this model excels
not_recommended_for	Jobs it's wrong for	Tasks where this model struggles
fence_compatibility	How well it respects boundaries	Conservation bytecode compliance

src/breed_registry/ — Python API

from breed_registry import select_breed, compare_breeds, assess_aptitude

# Find the best breed for a task
recommendations = select_breed("code_generation")

# Compare two breeds head-to-head
report = compare_breeds("gpt-4", "llama-3")

# Check a specific model's aptitude for a task
score = assess_aptitude("mistral", "analysis")

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Registered Breeds

Breed	Group	Cost	Speed	Best For
GPT-4	General Purpose	High	Moderate	Complex reasoning, code generation
[Claude-3](registry/claude-3.json	General Purpose	High	Moderate	Analysis, careful reasoning
Llama-3	Open Lineage	Free	Fast	Local deployment, fine-tuning
GLM	General Purpose	Low	Fast	Multilingual, efficient inference
Mistral	Working	Low	Fast	Efficient European-lineage tasks

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Selection Philosophy

1. Match the Breed to the Job

Don't use a high-cost generalist for simple classification. Don't use a lightweight model for complex multi-step reasoning. Match instincts to tasks.

2. Consider the Lineage

Model families carry traits across generations. Fine-tunes inherit strengths and weaknesses. Know the pedigree before you commit.

3. Respect the Fence

Conservation compliance — how well a model respects guardrails and bytecode fences — is non-negotiable for production work. A model that won't respect boundaries is a liability.

4. Trainability Matters

Some breeds take to fine-tuning naturally. Others resist. If you need to specialize, choose a breed known for trainability.

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Adding a Breed

To register a new model:

1. Create registry/<model-name>.json following the assessment schema
2. Add an entry to registry/index.json
3. Submit a pull request

Assess honestly. Overrating a model's working aptitude helps no one — least of all the teams who deploy it.

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License

MIT

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Part of the Working Animal Architecture — foundation models as working animals, not pets.