gnai-creator/noesis-decoder

library_name: transformers tags:

• symbolic-decoder
• aletheia
• pytorch
• onnx
• philosophical-agi
• gnai-creator license: apache-2.0 datasets:
• custom language:
• en pipeline_tag: text-generation

🧠 Noesis Decoder (AletheiaEngine)

Repository: gnai-creator/noesis-decoder Author: Felipe M. Muniz (gnai-creator) License: Apache-2.0

🔍 Overview

Noesis Decoder is the proprietary symbolic decoder of AletheiaEngine — a hybrid symbolic–neural system designed for philosophical artificial general intelligence.

Unlike conventional text generators, Noesis translates symbolic embeddings (ψₛ) into meaningful language based on epistemic coherence, rather than statistical prediction.

⚙️ Model Architecture

• Framework: PyTorch → ONNX Runtime

• Files:

  • model_infer.onnx – Inference model (optimized)
  • noesis.pt – PyTorch checkpoint (training artifact)
  • inference.py – Custom ONNX handler

• Input: float32 symbolic vector, shape [1, D]

• Output: decoded float or token embeddings (depending on context)

🧩 Example Usage

🔹 Python + ONNX Runtime

from huggingface_hub import hf_hub_download
import onnxruntime as ort
import numpy as np

# Download ONNX model
onnx_path = hf_hub_download(
    repo_id="gnai-creator/noesis-decoder",
    filename="model_infer.onnx",
    repo_type="model"
)

# Load runtime
sess = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
input_name  = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name

# Example symbolic vector ψₛ
x = np.random.randn(1, 300).astype("float32")

# Run inference
y = sess.run([output_name], {input_name: x})[0]
print("Output shape:", y.shape)

💡 Training Data

Trained on symbolic text pairs generated from philosophical, logical, and reflective corpora within the AletheiaEngine ecosystem. Goal: alignment between symbolic intention (ψₛ) and natural language output.

📊 Metrics (Indicative)

🚀 Deployment

This model is compatible with Hugging Face Inference Endpoints using the Custom engine and the included inference.py handler.

Recommended hardware:

• CPU: Intel Sapphire Rapids (1vCPU / 2GB)
• GPU: NVIDIA T4 for larger batch inference

⚠️ Limitations

• Not a conventional LLM — requires symbolic vectors as input.
• Outputs are contextualized to Aletheia’s symbolic reasoning pipeline.
• Not suited for free-form text generation.

📜 License

This repository is distributed under the Apache License 2.0. See LICENSE for details.

“Truth is not imposed; it emerges from alignment.” — Felipe M. Muniz (2025)