t81-model-loader

name	t81_model_loader
description	Load external AI models into T81 with secure conversion and immutable storage
metadata	{"openclaw":{"os":["darwin","linux"],"requires":{"bins":["t81"]}}}

T81 Model Loader Skill

This skill loads external AI models into T81's ecosystem with secure conversion, deterministic validation, and immutable CanonFS storage.

Usage

When the user needs to import external AI models (Hugging Face, GGUF, etc.) into T81, use this skill for secure model conversion and storage.

Commands

Load Hugging Face model:

load-hf <model_name_or_path> [--output <output_file>] [--canonfs-root <path>] [--policy <policy_file>]

Load GGUF model:

load-gguf <gguf_file> [--output <output_file>] [--canonfs-root <path>] [--validate]

Load SafeTensors model:

load-safetensors <safetensors_file> [--output <output_file>] [--canonfs-root <path>]

Probe model structure:

probe-model <model_file> [--detailed] [--layer-info] [--output <probe_file>]

Validate model integrity:

validate-model <model_file> [--check-determinism] [--security-scan] [--output <report_file>]

Convert model format:

convert-model <input_file> [--to-format t81w|safetensors|gguf] [--output <output_file>]

Batch model processing:

batch-load <model_directory> [--recursive] [--output-dir <dir>] [--canonfs-root <path>]

Examples

# Load Hugging Face model with policy
load-hf "bert-base-uncased" --output bert.t81w --policy model_import.apl

# Load GGUF model with validation
load-gguf llama-2-7b.gguf --canonfs-root ~/.t81_canonfs --validate

# Probe model structure
probe-model model.t81w --detailed --layer-info --output model_structure.json

# Validate model integrity and determinism
validate-model weights.t81w --check-determinism --security-scan

# Convert between formats
convert-model model.safetensors --to-format t81w --output model.t81w

# Batch process model directory
batch-load ./models/ --recursive --output-dir ./converted/ --canonfs-root ./canonfs/

Supported Model Formats

Input Formats:

• Hugging Face: Hub models and local checkpoints
• SafeTensors: Native safe tensor format
• GGUF: GGUF format (quantized models)
• PyTorch: .pth and .pt files
• TensorFlow: SavedModel and .pb files
• ONNX: .onnx model files

Output Formats:

• T81W: T81 native ternary weights format
• SafeTensors: Universal safe format
• GGUF: Compressed quantized format

Model Conversion Pipeline

1. Model Analysis:

• Architecture detection
• Layer structure mapping
• Parameter counting
• Memory requirements

2. Security Validation:

• Malware scanning
• Code injection detection
• Signature verification
• Policy compliance checking

3. Deterministic Conversion:

• Bit-identical parameter conversion
• Ternary precision optimization
• Deterministic layout generation
• Hash verification

4. CanonFS Storage:

• Immutable storage
• Content-addressed hashing
• Complete provenance
• Policy-gated access

Output Format

Model Loading Result:

{
  "status": "success",
  "model_name": "bert-base-uncased",
  "input_format": "huggingface",
  "output_file": "bert.t81w",
  "canonfs_hash": "CanonHash81_ABC123...",
  "timestamp": "2026-04-04T15:30:00Z",
  "model_info": {
    "architecture": "BERT",
    "parameters": 110000000,
    "layers": 12,
    "hidden_size": 768,
    "vocab_size": 30522
  },
  "conversion": {
    "precision_preserved": true,
    "determinism_verified": true,
    "ternary_optimized": true,
    "compression_ratio": 0.85
  },
  "security": {
    "scanned": true,
    "threats_detected": 0,
    "policy_compliant": true,
    "signature_verified": true
  },
  "provenance": {
    "source": "huggingface_hub",
    "import_version": "1.9.0",
    "conversion_hash": "CanonHash81_CONV...",
    "policy_hash": "CanonHash81_POLICY..."
  }
}

Model Probe Result:

{
  "model_file": "bert.t81w",
  "timestamp": "2026-04-04T15:30:00Z",
  "architecture": "BERT",
  "layers": [
    {
      "name": "embeddings.word_embeddings",
      "type": "embedding",
      "shape": "[30522, 768]",
      "dtype": "ternary_float",
      "parameters": 23440896
    },
    {
      "name": "encoder.layer.0.attention.self.query",
      "type": "linear",
      "shape": "[768, 768]",
      "dtype": "ternary_float",
      "parameters": 589824
    }
  ],
  "total_parameters": 110000000,
  "model_size_mb": 420,
  "ternary_optimized": true,
  "determinism_guaranteed": true
}

Security Features

Model Scanning:

• Static code analysis
• Malware detection
• Vulnerability scanning
• Signature verification

Policy Enforcement:

• Import permission checking
• Model type validation
• Size and complexity limits
• Source authorization

Access Control:

• User authentication
• Role-based permissions
• Audit trail maintenance
• Secure storage

Determinism Guarantees

Conversion Determinism:

• Bit-identical parameter conversion
• Reproducible model loading
• Consistent hash generation
• Deterministic layout

Execution Determinism:

• Same model produces same outputs
• Cross-platform consistency
• Temporal stability
• Verifiable inference

Performance Considerations

Large Model Handling:

• Streaming conversion for large models
• Memory-efficient processing
• Progress reporting
• Resume capability

Optimization Features:

• Ternary precision optimization
• Memory layout optimization
• Compression opportunities
• Cache-friendly structures

Error Handling

• Format Errors: Unsupported format detection and suggestions
• Corruption Errors: Model integrity validation and recovery
• Security Errors: Threat detection and quarantine procedures
• Policy Errors: Compliance violation explanations

Integration

This skill wraps T81's model loading and conversion pipeline:

t81 model load <source> --format <format> --output <file> --canonfs-root <path> --policy <policy>