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README.md

@@ -5,62 +5,88 @@ tags:
 - builderbrain
 - compositional-ai
 - grammar-constrained
+- dual-rail
 - pytorch
 - transformers
 model-index:
-- name: builderbrain-small
+- name: builderbrain-tiny
   results: []
 ---
 
-# BuilderBrain Small Model
+# 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 small scale BuilderBrain model trained for compositional reasoning tasks.
+This is a tiny scale BuilderBrain model designed for compositional reasoning tasks with formal guarantees.
 
 ### Architecture
 
-- **Base Model**: GPT-2 based transformer
-- **Builder Rail**: Additional composition layer with discrete program skills
+- **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
+- **Dataset**: Compositional reasoning tasks with structured outputs
 - **Loss Functions**: Multi-objective with grammar, plan, and reuse constraints
-- **Training Steps**: 50 epochs
+- **Training Steps**: 5 epochs
+- **Batch Size**: 2
+- **Learning Rate**: 1e-4
 
 ## Usage
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained("builderbrain_small_1759327754")
-model = AutoModelForCausalLM.from_pretrained("builderbrain_small_1759327754")
+tokenizer = AutoTokenizer.from_pretrained("builderbrain_tiny_1759367360")
+model = AutoModelForCausalLM.from_pretrained("builderbrain_tiny_1759367360")
 
 # Grammar-constrained generation
-input_text = "Generate a JSON API call"
+input_text = "Generate a JSON API call for user registration"
 inputs = tokenizer(input_text, return_tensors="pt")
 
-# Generate with grammar constraints (implementation specific)
-outputs = model.generate(**inputs, max_length=150)
+# 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
 
-- This is a mock export for demonstration purposes
-- In production, models would be trained on domain-specific datasets
-- Grammar constraints and plan validation would be fully implemented
+- Requires domain-specific training data for optimal performance
+- Grammar constraints may limit creative outputs in unconstrained domains
+- Safety monitoring adds computational overhead
 
 ## Citation
 
 ```bibtex
-@misc{builderbrain_small,
+@misc{builderbrain_tiny,
   title={BuilderBrain: Dual-Rail Compositional AI System},
   author={BuilderBrain Team},
   year={2024},