Update README.md

README.md

@@ -61,6 +61,21 @@ seq = torch.randn(1, 1024)  # (batch, time)
 pred_median, pred_quantiles = model(seq, forecast_horizon=forecast_horizon, quantiles=[0.1, 0.5, 0.9])  # (batch, time, quantiles)
 ```
 
+### from pip package
+
+1. Install the package from PyPI
+```bash
+pip install patchfm
+```
+2. Run inference with a pretrained model from Huggingface Hub
+
+```python 
+import torch
+from patchfm import PatchFMConfig, Forecaster
+
+# same as above
+```
+
 We provide an extended quick start example in [notebooks/tutorial.ipynb](./notebooks/tutorial.ipynb).
 If you dont have suitable hardware you can run the the extended quick start example example also in Google Colab:
 
@@ -74,7 +89,7 @@ If you dont have suitable hardware you can run the the extended quick start exam
 - Architecture: Input residual MLP → stacked Transformer blocks (MHA + SwiGLU FFN, pre-norm, residual) → $|\mathcal{Q}|$ output heads mapping back to patch space.
 - Positional encoding: Rotary Position Embeddings (RoPE) applied to queries/keys.
 - Training: Multi-quantile (pinball) loss across positions, elements, and quantiles $\mathcal{Q}$.
-- Inference: Predict next patch; roll out autoregressively with KV caching for long horizons.
+- Inference: Predict next patch; roll out autoregressively for long horizons.
 
 ## Problem Formulation
 Given context patches $x_{p_1}, \ldots, x_{p_n}$, predict the next patch $x_{p_{i+1}}$ for each position $i$ using only past patches (causality). The model outputs quantiles $\{\hat{x}_{p_{i+1}}^{(q)}: q \in \mathcal{Q}\}$ with median (q=0.5) as the point forecast.
@@ -105,7 +120,9 @@ Aggregate over positions, patch elements, and quantiles.
 - Long-horizon: append prediction to context and repeat (optionally drop oldest patch to keep window fixed)
 
 ## Datasets
-- UTSD (Unified Time Series Dataset) [UTSD]: seven domains (Energy, IoT, Nature, Web, Health, Transport, Environment). We start with UTSD-1G (~55M series after preprocessing).
+- UTSD (Unified Time Series Dataset) [UTSD]: seven domains (Energy, IoT, Nature, Web, Health, Transport, Environment). We work with UTSD-12G (~18M series after preprocessing).
+- GIFT-Eval pretraining dataset [GIFT]: aligned with the GIFT-Eval dataset but without data leakage issue with the benchmark. The dataset contains approximately 71 univariate and 17 multivariate time series datasets from various
+domains and various frequencies. After preprocessing, this yields approximately 600K univariate series.
 - Artificial: ~1M synthetic series (sinusoidal, linear, polynomial, logarithmic) plus mixtures via TSMixup [Chronos]; Gaussian Process samples via KernelSynth (mixtures of RBF/periodic/linear kernels with swept hyperparameters).
 
 ## Repository Layout
@@ -124,6 +141,7 @@ Aggregate over positions, patch elements, and quantiles.
 - `dataset/` — data loading and preprocessing
   - `artificial.py` — synthetic dataset : artificial signals + TSMixup + KernelSynth
   - `utsd.py` — Unified Time Series Dataset (UTSD) loading and preprocessing
+  - `gift.py` — GIFT-Eval pretraining dataset loading and preprocessing
   - `get_data.py` — utility to fetch and preprocess datasets
   - `generate_data.py` — utility to generate and save the KernelSynth dataset (long to generate)