Instructions to use chifural/Mhondoro-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chifural/Mhondoro-mini with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chifural/Mhondoro-mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Initial Mhondoro release (mirrored + rebranded)
Browse files- README.md +59 -0
- config.json +13 -0
- model.safetensors +3 -0
README.md
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---
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license: mit
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tags:
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- finance
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- time-series
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- forecasting
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- candlestick
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- kline
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- foundation-model
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library_name: transformers
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---
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# Mhondoro-mini
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`Mhondoro-mini` is part of the **Mhondoro** family of open-source foundation models for
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financial time-series forecasting, released by [Louis Chifura](https://huggingface.co/chifural).
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Mhondoro models tokenize OHLCV candlestick (K-line) sequences and predict future
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candles autoregressively. They are designed as a unified base for downstream
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quantitative tasks such as price forecasting, volatility prediction, and regime detection.
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## Model family
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| Model | Tokenizer | Context | Params |
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|---|---|---|---|
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| Mhondoro-mini | Mhondoro-Tokenizer-2k | 2048 | 4.1M |
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| Mhondoro-small | Mhondoro-Tokenizer-base | 512 | 24.7M |
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| Mhondoro-base | Mhondoro-Tokenizer-base | 512 | 102.3M |
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## Quick start
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```python
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from model.mhondoro import Mhondoro, MhondoroTokenizer, MhondoroPredictor
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tokenizer = MhondoroTokenizer.from_pretrained("chifural/Mhondoro-Tokenizer-base")
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model = Mhondoro.from_pretrained("chifural/Mhondoro-small")
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predictor = MhondoroPredictor(model=model, tokenizer=tokenizer, device="cuda:0")
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forecast = predictor.predict(
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df=ohlcv_dataframe,
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x_timestamp=context_timestamps,
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y_timestamp=forecast_timestamps,
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pred_len=120,
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)
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```
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Source code: [github.com/vigilancetrent/mhondoro](https://github.com/vigilancetrent/mhondoro).
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## License
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MIT. Copyright (c) 2026 Louis Chifura.
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## Notice
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This work builds on the architectural design of the open-source NeoQuasar/Kronos
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project (MIT, Copyright (c) 2025 ShiYu). The Mhondoro fork extends the original
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with additional fine-tuning workflows, broader market coverage, and an evolving
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feature set. See the upstream project at https://github.com/Shiyu-Coder/Kronos
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for the historical record.
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config.json
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{
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"attn_dropout_p": 0.0,
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"d_model": 256,
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"ff_dim": 512,
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"ffn_dropout_p": 0.2,
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"learn_te": true,
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"n_heads": 4,
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"n_layers": 4,
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"resid_dropout_p": 0.2,
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"s1_bits": 10,
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"s2_bits": 10,
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"token_dropout_p": 0.0
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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:a7d5f37e2e9fbd9891f7d7d4f72574512dd1f704fee14223e0a8cd0fbf54197c
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size 16440776
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