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---
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library_name: transformers
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tags:
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- symbolic-decoder
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- aletheia
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- pytorch
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- onnx
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- philosophical-agi
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- gnai-creator
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license: apache-2.0
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datasets:
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- custom
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language:
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- en
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pipeline_tag: text-generation
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---
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# 🧠 Noesis Decoder (AletheiaEngine)
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**Repository:** [gnai-creator/noesis-decoder](https://huggingface.co/gnai-creator/noesis-decoder)
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**Author:** Felipe M. Muniz (`gnai-creator`)
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**License:** Apache-2.0
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---
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## 🔍 Overview
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**Noesis Decoder** is the proprietary symbolic decoder of **AletheiaEngine** — a hybrid symbolic–neural system designed for *philosophical artificial general intelligence*.
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Unlike conventional text generators, Noesis translates **symbolic embeddings (ψₛ)** into meaningful language based on *epistemic coherence*, rather than statistical prediction.
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---
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## ⚙️ Model Architecture
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* **Framework:** PyTorch → ONNX Runtime
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* **Files:**
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* `model_infer.onnx` – Inference model (optimized)
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* `noesis.pt` – PyTorch checkpoint (training artifact)
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* `inference.py` – Custom ONNX handler
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* **Input:** float32 symbolic vector, shape `[1, D]`
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* **Output:** decoded float or token embeddings (depending on context)
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---
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## 🧩 Example Usage
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### 🔹 Python + ONNX Runtime
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```python
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from huggingface_hub import hf_hub_download
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import onnxruntime as ort
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import numpy as np
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# Download ONNX model
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onnx_path = hf_hub_download(
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repo_id="gnai-creator/noesis-decoder",
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filename="model_infer.onnx",
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repo_type="model"
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)
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# Load runtime
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sess = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
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input_name = sess.get_inputs()[0].name
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output_name = sess.get_outputs()[0].name
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# Example symbolic vector ψₛ
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x = np.random.randn(1, 300).astype("float32")
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# Run inference
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y = sess.run([output_name], {input_name: x})[0]
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print("Output shape:", y.shape)
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```
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---
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## 💡 Training Data
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Trained on **symbolic text pairs** generated from philosophical, logical, and reflective corpora within the AletheiaEngine ecosystem.
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Goal: alignment between **symbolic intention (ψₛ)** and **natural language output**.
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---
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## 📊 Metrics (Indicative)
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| Metric | Value | Description |
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| ------------- | ------------ | ------------------------------------------ |
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| Cosine(Q) | 0.83 | Symbolic alignment measure |
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| Perplexity | 2.41 | Statistical readability proxy |
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| Latency (CPU) | ~28 ms/token | Inference on Intel Sapphire Rapids (1vCPU) |
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---
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## 🚀 Deployment
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This model is compatible with **Hugging Face Inference Endpoints** using the `Custom` engine and the included `inference.py` handler.
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Recommended hardware:
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* **CPU:** Intel Sapphire Rapids (1vCPU / 2GB)
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* **GPU:** NVIDIA T4 for larger batch inference
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---
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## ⚠️ Limitations
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* Not a conventional LLM — requires symbolic vectors as input.
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* Outputs are contextualized to Aletheia’s symbolic reasoning pipeline.
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* Not suited for free-form text generation.
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---
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## 📜 License
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This repository is distributed under the **Apache License 2.0**.
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See [LICENSE](./LICENSE) for details.
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---
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> *“Truth is not imposed; it emerges from alignment.”*
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> — *Felipe M. Muniz (2025)*
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