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
| 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. | |