Update README.md

README.md

@@ -26,21 +26,37 @@ tags:
 pip install transformers==4.30.2 # Use this version for stable compatibility
 ```
 
+### Zero-Shot
 ```
+from configuration_LightGTS import LightGTSConfig
+from modeling_LightGTS import LightGTSForPrediction
 import torch
 from transformers import AutoModelForCausalLM
+from transformers import AutoModelForCausalLM, MODEL_MAPPING
+from transformers import AutoConfig
 # load pretrain model
-# supports different lookback/forecast lengths
-model = AutoModelForCausalLM.from_pretrained('DecisionIntelligence/LightGTS', trust_remote_code=True) 
+LightGTS_config = LightGTSConfig(context_points=528, c_in=1, target_dim=192, patch_len=48, stride=48)
+LightGTS_config.save_pretrained("LightGTS-huggingface")
+
+AutoConfig.register("LightGTS",LightGTSConfig)
+AutoModelForCausalLM.register(LightGTSConfig, LightGTSForPrediction)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "./LightGTS-huggingface",
+    trust_remote_code=True
+)
 # prepare input
-batch_size, lookback_length = 1, 528
+batch_size, lookback_length = 1, 576
 seqs = torch.randn(batch_size, lookback_length).unsqueeze(-1).float()
-# Note that Sundial can generate multiple probable predictions
+# generate forecasting results
 forecast_length = 192
 outputs = model.generate(seqs, patch_len = 48, stride_len=48, max_output_length=forecast_length, inference_patch_len=48)
-print(output.shape) 
+print(outputs.shape)
+
 ```
 
+### Fine-tune
+For usage examples, please see test_finetune.py
 
 ## Citation