Instructions to use 0x4C57/openeden-codebook-base-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use 0x4C57/openeden-codebook-base-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("0x4C57/openeden-codebook-base-model") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 1,092 Bytes
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license: agpl-3.0
library_name: sentence-transformers
tags:
- openeden
- codebook
- vector-quantization
- bilingual
---
# OpenEden Codebook Base Model
This repository stores the OpenEden 8D physiological vector to semantic
codebook alignment model. It is released under AGPL-3.0 together with the
OpenEden runtime.
## Contents
- `text_encoder/`: fine-tuned `Qwen/Qwen3-Embedding-0.6B` SentenceTransformer
- `vector_projector.pt`: 8D vector projector into the text embedding space
- `metadata.json`: training configuration
- `evaluation.json`: retrieval evaluation metrics
- `local-model-artifact.json`: JVM runtime artifact used by the local CLI
- `LICENSE`: GNU Affero General Public License v3.0
## Training
- samples: 2048
- codebook nodes: 512
- base model: `Qwen/Qwen3-Embedding-0.6B`
- epochs: 5
- batch size: 16
- text encoder: unfrozen
## Evaluation
- exactTop1: 0.12939453125
- nodeTop1: 0.509765625
- nodeTop5: 0.65478515625
The stored vector dimensions are exactly `[L, P, E, S, tau, V, M, F]`.
Derived dissonance is not stored in this model or training data.
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