metadata
language: en
license: apache-2.0
tags:
- builderbrain
- compositional-ai
- grammar-constrained
- dual-rail
- pytorch
- transformers
model-index:
- name: builderbrain-tiny
results: []
BuilderBrain Tiny Model
BuilderBrain is a dual-rail compositional AI system that extends pretrained transformers with learned composition blocks, grammar constraints, and executable plans.
Model Description
This is a tiny scale BuilderBrain model designed for compositional reasoning tasks with formal guarantees.
Architecture
- Base Rail: Frozen pretrained transformer (gpt2)
- Builder Rail: Additional composition layer with 8 discrete program skills
- Grammar Constraints: CFG/PEG parsing for structured outputs
- Plan Validation: DAG-based plan execution with precondition checking
- Multi-objective Training: Lagrangian optimization with constraint satisfaction
- Safety Monitoring: Risk energy prediction and violation detection
Model Specifications
- Hidden Size: 768
- Builder Layers: 4
- Program Skills: 8
- Alpha Cap: 0.05
- Grammar Constraints: 2 active constraints
Training
- Dataset: Compositional reasoning tasks with structured outputs
- Loss Functions: Multi-objective with grammar, plan, and reuse constraints
- Training Steps: 5 epochs
- Batch Size: 2
- Learning Rate: 1e-4
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("builderbrain_tiny_1759367360")
model = AutoModelForCausalLM.from_pretrained("builderbrain_tiny_1759367360")
# Grammar-constrained generation
input_text = "Generate a JSON API call for user registration"
inputs = tokenizer(input_text, return_tensors="pt")
# Generate with grammar constraints and safety monitoring
outputs = model.generate(
**inputs,
max_length=150,
grammar_constraint=True,
safety_monitoring=True,
temperature=0.8
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Capabilities
- Compositional Reasoning: Combines discrete skills into complex behaviors
- Grammar Compliance: Generates syntactically correct structured outputs
- Safety Awareness: Monitors and prevents harmful outputs
- Planning: Uses world models for multi-step reasoning
- Constraint Satisfaction: Maintains formal guarantees during generation
Limitations
- Requires domain-specific training data for optimal performance
- Grammar constraints may limit creative outputs in unconstrained domains
- Safety monitoring adds computational overhead
Citation
@misc{builderbrain_tiny,
title={BuilderBrain: Dual-Rail Compositional AI System},
author={BuilderBrain Team},
year={2024},
url={https://github.com/JacobFV/builderbrain}
}