Update README.md

README.md

@@ -12,6 +12,21 @@ can be easily fine-tuned for your target data. Refer to our [paper](https://arxi
 
 **Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed in 1 GPU machine or in laptops too!!**
 
+
+## Benchmark Highlights:
+
+TTM outperforms pre-trained GPT4TS (NeurIPS 23) by …
+
+TTM outperforms pre-trained LLMTime (NeurIPS 23) by ..
+
+TTM outperforms pre-trained Time-LLM (NeurIPS 23) by ..
+
+TTM outperform pre-trained MOIRAI by …
+
+TTM outperforms other popular benchmarks by ….
+
+TTM also outperforms the hard statistical baselines (Statistical ensemble and S-Naive) in M4-hourly dataset which pretrained TS models are finding hard to outperform.
+
 ## Model Description
 
 TTM falls under the category of “focused pre-trained models”, wherein each pre-trained TTM is tailored for a particular forecasting 
@@ -35,20 +50,6 @@ only 3-6 hours using 6 A100 GPUs, as opposed to several days or weeks in traditi
 
 - Stay tuned for more models !
 
-## Benchmark Highlights:
-
-TTM outperforms pre-trained GPT4TS (NeurIPS 23) by …
-
-TTM outperforms pre-trained LLMTime (NeurIPS 23) by ..
-
-TTM outperforms pre-trained Time-LLM (NeurIPS 23) by ..
-
-TTM outperform pre-trained MOIRAI by …
-
-TTM outperforms other popular benchmarks by ….
-
-TTM also outperforms the hard statistical baselines (Statistical ensemble and S-Naive) in M4-hourly dataset which pretrained TS models are finding hard to outperform.
-
 ## Model Details
 
 For more details on TTM architecture and benchmarks, refer to our [paper](https://arxiv.org/pdf/2401.03955.pdf).