Update README.md

README.md

@@ -1,3 +1,16 @@
+# A tutorial on how to build a Foundation Model for Univariate Time Series Forecasting
+
+A concise, reproducible recipe for training a transformer-based, patch-to-patch forecasting model for univariate time series. The approach mirrors Large Language Model (LLM) practices (next-token → next-patch) while remaining lightweight compared to a classic LLM and practical.
+
+## Highlights
+- Next-patch prediction objective (autoregressive, causal)
+- Patch-based representation of time series (tokens ↔ patches)
+- Causal masking self-attention with RoPE (relative positions)
+- RevIN (Reversible Instance Normalization) with causal statistics
+- SwiGLU feed-forward networks
+- Multi-quantile outputs (median + uncertainty bands)
+- Efficient rollout with KV caching
+
 ---
 tags:
 - timeseries
@@ -13,21 +26,6 @@ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://h
 - Code: [GitHub](https://github.com/vilhess/PatchFM)
 - Paper: Incoming
 
-# A tutorial on how to build a Foundation Model for Univariate Time Series Forecasting
-
-[Huggingface Model Card](https://huggingface.co/vilhess/PatchFM)
-
-A concise, reproducible recipe for training a transformer-based, patch-to-patch forecasting model for univariate time series. The approach mirrors Large Language Model (LLM) practices (next-token → next-patch) while remaining lightweight compared to a classic LLM and practical.
-
-## Highlights
-- Next-patch prediction objective (autoregressive, causal)
-- Patch-based representation of time series (tokens ↔ patches)
-- Causal masking self-attention with RoPE (relative positions)
-- RevIN (Reversible Instance Normalization) with causal statistics
-- SwiGLU feed-forward networks
-- Multi-quantile outputs (median + uncertainty bands)
-- Efficient rollout with KV caching
-
 ## Installation
 ```bash
 git clone https://github.com/vilhess/PatchFM