feat: update README

README.md

@@ -17,8 +17,6 @@ tags:
 
 
 
-
-
 ## Introduction
 
 Aurora is a highly capable multimodal time series foundation model. Based on the **Modality-Guided Multi-head Self-Attention** and  **Prototype-Guided Flow Matching**, Aurora can effectively utilize the domain-specific knowledge contained in modalities and support generative probabilistic forecasting, thus covering versatile forecasting scenarios.
@@ -38,11 +36,12 @@ In this work, we pretrain Aurora in a cross-modality paradigm, which adopts Chan
 <img alt="intro" src="https://cdn-uploads.huggingface.co/production/uploads/66276727368ec2a0b933772c/d82jT96jiGD0QL9s8RYg-.png" width="100%"/>
 </div>
 
+
 ## Quickstart
 
 #### From pypi (recommended)
 
-We have publised Aurora on PyPi, **you can directly install it with one line of code!**
+We have published Aurora on PyPi, **you can directly install it with one line of code!**
 
 ```shell
 $ pip install aurora-model==0.1.0
@@ -50,15 +49,18 @@ $ pip install aurora-model==0.1.0
 
 Then you can use the Aurora model to make zero-shot probabilistic forecasting!
 
+##### Unimodal Time Series Forecasting
+
 ```python
 from aurora import load_model
 import os
+import torch
 # os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
 model = load_model()
 
 # prepare input
 batch_size, lookback_length = 1, 528 
-seqs = torch.randn(batch_size, lookback_length)
+seqs = torch.randn(batch_size, lookback_length).cuda()
 
 # Note that Sundial can generate multiple probable predictions
 forecast_length = 96 
@@ -77,6 +79,54 @@ print(output.shape)
 
 
 
+##### Multimodal Time Series Forecasting
+
+```python
+from aurora import load_model
+from einops import rearrange
+import os
+import torch
+# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+model = load_model()
+tokenizer = model.tokenizer
+
+# prepare input
+batch_size, n_vars, lookback_length, max_text_length = 1, 10, 528, 200
+seqs = torch.randn(batch_size, lookback_length, n_vars).cuda()
+
+text = "1983-09-12: The Federal Register provides a uniform system for making available to the public regulations and legal notices issued by federal agencies in the United States."
+
+tokenized_text = tokenizer(text, padding='max_length', truncation=True, max_length=max_text_length, return_tensors="pt")
+text_input_ids = tokenized_text['input_ids'].cuda()
+text_attention_mask = tokenized_text['attention_mask'].cuda()
+text_token_type_ids = tokenized_text.get('token_type_ids', torch.zeros_like(text_input_ids)).cuda()
+    
+batch_input_ids = text_input_ids.repeat(n_vars, 1)
+batch_attention_mask = text_attention_mask.repeat(n_vars, 1)
+batch_token_type_ids = text_token_type_ids.repeat(n_vars, 1)
+batch_x = rearrange(seqs, "b l c -> (b c) l")
+
+# Note that Sundial can generate multiple probable predictions
+forecast_length = 96 
+num_samples = 100
+
+
+# For inference_token_len, you can refer to LightGTS (Periodic Patching).
+# We recommend to use the period length as the inference_token_len.
+output = model.generate(inputs=batch_x,text_input_ids=batch_input_ids,
+											  text_attention_mask=batch_attention_mask,
+                        text_token_type_ids=batch_token_type_ids,
+  										  max_output_length=forecast_length, 
+  											num_samples=num_samples, 
+  											inference_token_len=48)
+
+
+# use raw predictions for mean/quantiles/confidence-interval estimation
+print(output.shape) 
+```
+
+
+
 #### From raw code
 
 We release the original code of Aurora in this repo. You can also download the pretrained checkpoints in our [huggingface](https://huggingface.co/DecisionIntelligence/Aurora) repo and put them in the folder: aurora/.
@@ -92,6 +142,7 @@ $ pip install transformers[torch]
 ```python
 from huggingface_hub import snapshot_download
 import os
+import torch
 # os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
 
 # --- Configuration ---
@@ -135,7 +186,7 @@ model = AuroraForPrediction.from_pretrained("./",trust_remote_code=True)
 
 # prepare input
 batch_size, lookback_length = 1, 528 
-seqs = torch.randn(batch_size, lookback_length)
+seqs = torch.randn(batch_size, lookback_length).cuda()
 
 # Note that Sundial can generate multiple probable predictions
 forecast_length = 96 
@@ -149,11 +200,10 @@ output = model.generate(inputs=seqs, max_output_length=forecast_length, num_samp
 
 # use raw predictions for mean/quantiles/confidence-interval estimation
 print(output.shape) 
+```
 
 
 
-```
-
 ## Experiments
 
 You should refer to our [github repo](https://github.com/decisionintelligence/Aurora) for the complete experimental pipelines. For benchmarking (TSFM-Bench, ProbTS, TimeMMD, TFB, and EPF), you can install additional packages based on the requirement files under folders, and the datasets can be fetched from this [link](https://drive.google.com/file/d/12tJk858WaoG7ZVSvUq8KU1oHfGNJrARF/view?usp=drive_link). All experimental results can be reproduced by running the scripts in the benchmark folder：
@@ -175,6 +225,8 @@ TFB/scripts/run_aurora_tfb.sh
 TFB/scripts/run_aurora_uni.sh
 ```
 
+
+
 ## Performance
 
 **Aurora ahieves consistent state-of-the-art performance on these 5 benchmarks:**