programasweights 0.1.0.dev2

Compile natural language specifications into neural programs that run locally via llama.cpp.

ProgramAsWeights

Compile natural language specifications into neural programs (.paw files) that run locally.

Programs are stored as weight blobs (KV cache prefix + optional LoRA adapters) interpreted by a small fixed model. No API calls needed at runtime — fully deterministic, local execution.

Installation

pip install programasweights

Quick Start

Run a Program

import programasweights as paw

# Load and run a compiled program
fn = paw.function("program_id_or_path.paw")
result = fn("Contact [e-mail] or [e-mail]")
print(result)  # ["[e-mail]", "[e-mail]"]

Compile a Program

import programasweights as paw

# Compile from natural language specification
paw.compile(
    "output.paw",
    spec="Extract all email addresses from text and return as JSON list",
    checkpoint_dir="path/to/trained/compiler",
)

LoRA Support (PEFT Compatible)

Already using PEFT for LoRA training? Convert to .paw in one line:

import programasweights as paw

# Standard PEFT workflow:
# model = get_peft_model(base_model, LoraConfig(r=16, target_modules=["q_proj", "v_proj"]))
# trainer.train()
# model.save_pretrained("my_adapter/")

# Convert to .paw:
paw.from_peft(
    "my_adapter/",       # Your PEFT checkpoint
    "sentiment.paw",     # Output .paw file
    spec="Classify sentiment as positive or negative",
    tags=["sentiment", "classification"],
    examples=[
        {"input": "Great movie!", "output": "positive"},
        {"input": "Terrible film.", "output": "negative"},
    ],
)

# Use it:
fn = paw.function("sentiment.paw")
print(fn("This is amazing!"))  # → "positive"

Load LoRA from a .paw file:

lora_weights, lora_config = paw.load_paw_lora("sentiment.paw")
print(lora_config)  # {"rank": 16, "alpha": 32, ...}

Or use save_lora_to_paw() directly if you have raw tensors instead of a PEFT checkpoint.

.paw File Format v2

A .paw file is a self-contained neural program that includes:

Component	Description	Required
KV cache prefix	Continuous program (prefix weights)	Optional
Pseudo-program	Discrete text instructions	Optional
LoRA adapter	Fine-tuned adapter weights	Optional
Generation config	Temperature, top_p, max_tokens	Optional
Metadata	Interpreter model, spec, author, tags	Required

Program Hub

Browse and share programs at hub.programasweights.com

Links

• Website: programasweights.com
• Documentation: programasweights.readthedocs.io
• GitHub: github.com/programasweights/programasweights
• Program Hub: hub.programasweights.com

License

MIT