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.gitattributes

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README.md

@@ -1,3 +1,57 @@
 ---
 license: apache-2.0
+tags:
+- time-series
+- forecasting
+- foundation-model
+- zero-shot
 ---
+
+# Kairos-10M: Adaptive Time Series Foundation Model
+
+## Model Description
+
+**Kairos-10M** is a 10-million parameter time series foundation model designed for zero-shot forecasting across diverse domains. It features adaptive tokenization and instance-specific positional encodings to handle heterogeneous time series data with varying information density.
+
+## Key Features
+
+- 🔀 **Mixture-of-Size Dynamic Patching (MoS-DP)**: Adaptively selects tokenization granularity based on local information density
+- 🔄 **Instance-adaptive Rotary Position Embedding (IARoPE)**: Tailors positional encodings to unique temporal characteristics of each series
+- 📊 **Zero-shot Forecasting**: Strong generalization across domains without fine-tuning
+- ⚡ **Efficient**: Superior performance with fewer parameters
+
+## Model Specifications
+
+- **Parameters**: ~10 million
+- **Training Data**: PreSTS corpus (300+ billion time points)
+- **Architecture**: Transformer-based with adaptive components
+
+## Usage
+
+```python
+from kairos import KairosModel
+
+# Load model
+model = KairosModel.from_pretrained("Kairos_10m")
+
+# Zero-shot forecasting
+forecasts = model.predict(historical_data, prediction_length=96)
+```
+
+For detailed usage examples, please refer to the [main repository](https://github.com/foundation-model-research/Kairos).
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@article{kairos2025,
+  title={Kairos: Towards Adaptive and Generalizable Time Series Foundation Models},
+  author={Kun Feng and Shaocheng Lan and Yuchen Fang and Wenchao He and Lintao Ma and Xingyu Lu and Kan Ren},
+  year={2025}
+}
+```
+
+## License
+
+Apache License 2.0

config.json

@@ -0,0 +1,84 @@
+{
+  "architectures": [
+    "KairosModel"
+  ],
+  "classifier_dropout": 0,
+  "context_length": 2048,
+  "cross_attention_pe_flip": false,
+  "d_ff": 1024,
+  "d_kv": 64,
+  "d_model": 256,
+  "data_driven": true,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "diff_decoder_token_id": false,
+  "dropout_rate": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "finetune": false,
+  "initializer_factor": 0.05,
+  "input_patch_size": 128,
+  "input_patch_stride": 128,
+  "instance_rope_input_feature_dim": 128,
+  "is_cross_attention_pe": true,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "levels": 3,
+  "loss_weight_scheme": "log_decay",
+  "max_period": "original_rope_init",
+  "min_period": "original_rope_init",
+  "model_type": "kairos",
+  "moe_inter_dim": 1408,
+  "multi_pred_head": false,
+  "n_activated_experts": 3,
+  "n_expert_groups": 1,
+  "n_limited_groups": 1,
+  "n_null_experts": 2,
+  "n_positions": 512,
+  "num_decoder_layers": 4,
+  "num_decoder_segments": 2,
+  "num_heads": 4,
+  "num_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "instance_wise_rope",
+  "prediction_length": 64,
+  "pretrained_model_path": "",
+  "quantiles": [
+    0.1,
+    0.2,
+    0.3,
+    0.4,
+    0.5,
+    0.6,
+    0.7,
+    0.8,
+    0.9
+  ],
+  "reg_token_id": 1,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "rope_init": "exp",
+  "router_gumbel_softmax": false,
+  "scale_method": "log",
+  "score_func": "softmax",
+  "seq_balance_factor": 0.0001,
+  "target_dist": [
+    0.05,
+    0.1,
+    0.55,
+    0.15,
+    0.15
+  ],
+  "threshold": 0.6,
+  "transformers_version": "4.56.1",
+  "update_bias_rate": 0.01,
+  "use_bias": true,
+  "use_cache": true,
+  "use_reg_token": true,
+  "use_top1_bias": true,
+  "use_topk": true,
+  "vocab_size": 2,
+  "weights_norm": true
+}

model.safetensors

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