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ffurfaro/kairos
kairos is a text generation model from ffurfaro. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
Downloads · 30 days
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From the Hugging Face model README
KairosFM is a hybrid MoE diffusion language model combining DeltaNet (linear attention), Sliding Window Attention, and Attention Residuals (AttnRes), trained on text, image, video, audio, lidar, and control (state/action) modalities through a shared multimodal conv-byte tokenizer. See github.com/fabienfrfr/Kairos for the full architecture writeup.
| Total params | ?-dim, ? layers |
| Experts | 7 routed / 1 shared, top-1 |
| Vocab size | 291 |
| Best training loss | 7.30881994911411 |
| Steps trained | 4533 |
Note: this repo currently tracks best-training-loss only (checkpoints/best.pt) — no held-out
validation split is evaluated during training yet.
checkpoints/ — best.pt (lowest avg training loss) + periodic step_*.pttensorboard/ — events.out.tfevents.*, viewable in the Hub's Training Metrics tabconfig.json, model.safetensors — native HF format, loadable via trust_remote_codefrom transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ffurfaro/kairos", trust_remote_code=True)
Requires the kairos package importable (custom architecture, not upstream transformers) —
install from github.com/fabienfrfr/Kairos first, or add
it to PYTHONPATH. Alternatively, skip Auto* and import the class directly:
from kairos.modeling import KairosDiffusionLLM
model = KairosDiffusionLLM.from_pretrained("ffurfaro/kairos")
Experimental, low-compute-budget training run — expect uneven quality across modalities (multimodal data is a small fraction of total training). Not evaluated for safety-critical use.
@misc{kairos,
title = {KairosFM: less parameters, more signal — a multimodal MoE diffusion model for edge AI},
author = {Fabien Furfaro},
url = {https://github.com/fabienfrfr/Kairos}
}