Instructions to use Shadowell/Kairos-base-crypto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KRONOS
How to use Shadowell/Kairos-base-crypto with KRONOS:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Upload Kairos checkpoint
Browse files- README.md +52 -0
- config.json +18 -0
- model.safetensors +3 -0
README.md
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---
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license: mit
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tags: [time-series, finance, kronos, kairos, crypto]
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library_name: pytorch
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---
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# Shadowell/Kairos-base-crypto
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Fine-tuned **Kronos-Tokenizer-base** (crypto (BTC/USDT + ETH/USDT 1-min)), produced by
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**[Kairos](https://github.com/Shadowell/Kairos)**.
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The tokenizer encodes a rolling 6-dim OHLCV+amount window into two streams of
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discrete tokens (BSQ — Binary Spherical Quantization) that Kronos predictors
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consume downstream. Only the OHLCV inputs are used for training; the 32-dim
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exogenous channel required by `KronosWithExogenous` is not needed for the
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tokenizer itself.
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## Results on test set (304,770 windows, 2 symbols)
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| metric | baseline | finetuned | Δ |
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|---|---|---|---|
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| recon_mse_full | 0.005504 | 0.004141 | -24.8% |
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| recon_mae_full | 0.055000 | 0.047185 | -14.2% |
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| bsq_loss_mean | -0.070294 | -0.070731 | +0.6% |
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| s1 util | 0.6904 | 0.7314 | +5.9% |
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| s2 util | 0.4326 | 0.4277 | -1.1% |
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| s1 entropy (bits) | 5.9374 | 6.1164 | +3.0% |
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| s2 entropy (bits) | 4.6063 | 4.7127 | +2.3% |
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| channel | baseline | finetuned | Δ |
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|---|---|---|---|
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| open | 0.00528 | 0.00332 | -37.0% |
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| high | 0.00556 | 0.00504 | -9.5% |
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| low | 0.00576 | 0.00502 | -12.9% |
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| close | 0.00437 | 0.00308 | -29.6% |
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| vol | 0.00615 | 0.00420 | -31.8% |
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| amt | 0.00589 | 0.00420 | -28.8% |
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## Usage
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```python
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from kairos import KronosTokenizer
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tok = KronosTokenizer.from_pretrained("Shadowell/Kairos-base-crypto")
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# Encode a [B, T, 6] OHLCV tensor into (s1_ids, s2_ids)
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s1, s2 = tok.encode(x, half=True)
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```
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## Training recipe
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See `docs/CRYPTO_TOKENIZER_RUN.md` in the upstream repo for the full
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reproduction checklist (data collection, preparation, 15-epoch + patience 3
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fine-tune, reconstruction evaluation).
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config.json
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{
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"attn_dropout_p": 0.0,
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"beta": 0.05,
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"d_in": 6,
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"d_model": 256,
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"ff_dim": 512,
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"ffn_dropout_p": 0.0,
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"gamma": 1.1,
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"gamma0": 1.0,
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"group_size": 4,
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"n_dec_layers": 4,
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"n_enc_layers": 4,
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"n_heads": 4,
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"resid_dropout_p": 0.0,
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"s1_bits": 10,
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"s2_bits": 10,
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"zeta": 0.05
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9207551f3a7c1b66e5517602c3b7becbfd915acbb2dad9c96fd13812a1d93779
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size 15842368
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