[FIX] Img links

nbs/docs/getting-started/1_introduction.ipynb

@@ -52,7 +52,7 @@
     "\n",
     "Self-attention, the revolutionary concept introduced by the paper [Attention is all you need](https://arxiv.org/abs/1706.03762), is the basis of this foundation model. TimeGPT model is not based on any existing large language model(LLM). Instead, it is independently trained on a vast amount of time series data, and the large transformer model is designed to minimize the forecasting error.\n",
     "\n",
-    "<img src=\"../../img/timegpt_archi.png\" />\n",
+    "<img src=\"https://github.com/Nixtla/nixtla/blob/main/nbs/img/timegpt_archi.png?raw=true\" />\n",
     "\n",
     "The architecture consists of an encoder-decoder structure with multiple layers, each with residual connections and layer normalization. Finally, a linear layer maps the decoder’s output to the forecasting window dimension. The general intuition is that attention-based mechanisms are able to capture the diversity of past events and correctly extrapolate potential future distributions."
    ]

nbs/docs/getting-started/7_why_timegpt.ipynb

@@ -761,7 +761,7 @@
     "#### Benchmark Results\n",
     "For a more comprehensive dive into model accuracy and performance, explore our [Time Series Model Arena](https://github.com/Nixtla/nixtla/tree/main/experiments/foundation-time-series-arena)! TimeGPT continues to lead the pack with exceptional performance across benchmarks! 🌟\n",
     "\n",
-    "<img src=\"../../assets/timeseries_model_arena.png\" />"
+    "<img src=\"https://github.com/Nixtla/nixtla/blob/main/nbs/img/timeseries_model_arena.png?raw=true\" alt=\"Benchmark\" />"
    ]
   },
   {