federicosabbadini
/

patch-aliasing-models

@@ -1,83 +1,84 @@
----
-language: en
-license: apache-2.0
-tags:
-  - time-series
-  - forecasting
-  - chronos
-  - patch-aliasing
-base_model: amazon/chronos-bolt-tiny
----
-# Chronos-Bolt Patch-Stride Sweep
-Retrained variants of [Chronos-Bolt Tiny](https://huggingface.co/amazon/chronos-bolt-tiny) (~8.7M params) with different **patch size (P)** and **patch stride (S)** configurations, trained from scratch on the full official Chronos pre-training data.
-## Purpose
-These models support the **structural patch-aliasing** study: investigating how the (P, S) patch geometry of Chronos-Bolt creates frequency-dependent artifacts in time-series forecasting. Each model is trained identically except for the patch geometry, so any downstream difference in aliasing probes is attributable to (P, S).
-## Models
-| Subfolder | P | S | Overlap | Status |
-|---|---|---|---|---|
-| `p8-s8-seed42` | 8 | 8 | 0.000 | done |
-| `p16-s4-seed42` | 16 | 4 | 0.750 | done |
-| `p16-s8-seed42` | 16 | 8 | 0.500 | done |
-| `p16-s12-seed42` | 16 | 12 | 0.250 | done |
-| `p16-s15-seed42` | 16 | 15 | 0.062 | done |
-| `p16-s16-seed42` | 16 | 16 | 0.000 | done |
-| `p24-s8-seed42` | 24 | 8 | 0.667 | done |
-| `p24-s12-seed42` | 24 | 12 | 0.500 | done |
-| `p24-s15-seed42` | 24 | 15 | 0.375 | done |
-| `p24-s16-seed42` | 24 | 16 | 0.333 | done |
-| `p24-s20-seed42` | 24 | 20 | 0.167 | done |
-| `p24-s24-seed42` | 24 | 24 | 0.000 | done |
-| `p32-s8-seed42` | 32 | 8 | 0.750 | done |
-| `p32-s12-seed42` | 32 | 12 | 0.625 | done |
-| `p32-s15-seed42` | 32 | 15 | 0.531 | done |
-| `p32-s16-seed42` | 32 | 16 | 0.500 | done |
-| `p32-s20-seed42` | 32 | 20 | 0.375 | done |
-| `p32-s24-seed42` | 32 | 24 | 0.250 | done |
-| `p32-s28-seed42` | 32 | 28 | 0.125 | done |
-| `p32-s32-seed42` | 32 | 32 | 0.000 | done |
-## Training setup
-All models share the same training regime (only P and S vary):
-- **Architecture**: Chronos-Bolt Tiny (T5-based, ~8.7M params), random initialization
-- **Data**: Official Chronos pre-training corpus — TSMixup (10M series) + KernelSynth (1M series) at 9:1 ratio
-- **Steps**: 100,000 (fixed budget across all runs)
-- **Optimizer**: AdamW, lr=1e-3, linear decay, no warmup
-- **Batch size**: 32
-- **Precision**: fp32 + TF32 matmuls (Ampere+ GPUs)
-- **Context**: 2048 tokens, prediction horizon: 64
-- **Quantiles**: 9 (0.1 to 0.9)
-- **Seed**: 42
-Full provenance is recorded in each subfolder's `run_config.json`.
-## Usage
-```python
-from chronos import BaseChronosPipeline
-pipe = BaseChronosPipeline.from_pretrained(
-    "federicosabbadini/chronos-bolt-patch-sweep",
-    subfolder="p24-s16-seed42",
-    device_map="cpu",
-)
-```
-## Files per model
-- `config.json` — model architecture config
-- `model.safetensors` — trained weights (~34 MB)
-- `run_config.json` — full training provenance + result metrics
-- `loss_history.npy` — per-step training loss
-- `loss_curve.png` — training loss plot
-## License
-The models are derived from the Chronos architecture (Apache-2.0) and trained on the official Chronos datasets. See the original [Chronos repository](https://github.com/amazon-science/chronos-forecasting) for details.

+---
+language: en
+license: apache-2.0
+tags:
+  - time-series
+  - forecasting
+  - chronos
+  - patch-aliasing
+base_model: amazon/chronos-bolt-tiny
+---
+# Chronos-Bolt Patch-Stride Sweep
+Retrained variants of [Chronos-Bolt Tiny](https://huggingface.co/amazon/chronos-bolt-tiny) (~8.7M params) with different **patch size (P)** and **patch stride (S)** configurations, trained from scratch on the full official Chronos pre-training data.
+## Purpose
+These models support the **structural patch-aliasing** study: investigating how the (P, S) patch geometry of Chronos-Bolt creates frequency-dependent artifacts in time-series forecasting. Each model is trained identically except for the patch geometry, so any downstream difference in aliasing probes is attributable to (P, S).
+## Models
+| Subfolder | P | S | Overlap | Status |
+|---|---|---|---|---|
+| `p8-s4-seed42` | 8 | 4 | 0.500 | done |
+| `p8-s8-seed42` | 8 | 8 | 0.000 | done |
+| `p16-s4-seed42` | 16 | 4 | 0.750 | done |
+| `p16-s8-seed42` | 16 | 8 | 0.500 | done |
+| `p16-s12-seed42` | 16 | 12 | 0.250 | done |
+| `p16-s15-seed42` | 16 | 15 | 0.062 | done |
+| `p16-s16-seed42` | 16 | 16 | 0.000 | done |
+| `p24-s8-seed42` | 24 | 8 | 0.667 | done |
+| `p24-s12-seed42` | 24 | 12 | 0.500 | done |
+| `p24-s15-seed42` | 24 | 15 | 0.375 | done |
+| `p24-s16-seed42` | 24 | 16 | 0.333 | done |
+| `p24-s20-seed42` | 24 | 20 | 0.167 | done |
+| `p24-s24-seed42` | 24 | 24 | 0.000 | done |
+| `p32-s8-seed42` | 32 | 8 | 0.750 | done |
+| `p32-s12-seed42` | 32 | 12 | 0.625 | done |
+| `p32-s15-seed42` | 32 | 15 | 0.531 | done |
+| `p32-s16-seed42` | 32 | 16 | 0.500 | done |
+| `p32-s20-seed42` | 32 | 20 | 0.375 | done |
+| `p32-s24-seed42` | 32 | 24 | 0.250 | done |
+| `p32-s28-seed42` | 32 | 28 | 0.125 | done |
+| `p32-s32-seed42` | 32 | 32 | 0.000 | done |
+## Training setup
+All models share the same training regime (only P and S vary):
+- **Architecture**: Chronos-Bolt Tiny (T5-based, ~8.7M params), random initialization
+- **Data**: Official Chronos pre-training corpus — TSMixup (10M series) + KernelSynth (1M series) at 9:1 ratio
+- **Steps**: 100,000 (fixed budget across all runs)
+- **Optimizer**: AdamW, lr=1e-3, linear decay, no warmup
+- **Batch size**: 32
+- **Precision**: fp32 + TF32 matmuls (Ampere+ GPUs)
+- **Context**: 2048 tokens, prediction horizon: 64
+- **Quantiles**: 9 (0.1 to 0.9)
+- **Seed**: 42
+Full provenance is recorded in each subfolder's `run_config.json`.
+## Usage
+```python
+from chronos import BaseChronosPipeline
+pipe = BaseChronosPipeline.from_pretrained(
+    "federicosabbadini/chronos-bolt-patch-sweep",
+    subfolder="p24-s16-seed42",
+    device_map="cpu",
+)
+```
+## Files per model
+- `config.json` — model architecture config
+- `model.safetensors` — trained weights (~34 MB)
+- `run_config.json` — full training provenance + result metrics
+- `loss_history.npy` — per-step training loss
+- `loss_curve.png` — training loss plot
+## License
+The models are derived from the Chronos architecture (Apache-2.0) and trained on the official Chronos datasets. See the original [Chronos repository](https://github.com/amazon-science/chronos-forecasting) for details.