Datasets:

ibm-granite
/

GneissWeb

@@ -78,29 +78,26 @@ The Extended Task set have some tasks which are not in High Signal. These tasks
 These differences between the High-Signal Tasks vs Extended Tasks are seen in Fig 5 where we see a comparison of the High Signal Tasks vs those which are in the Extended Tasks and excluded from the High Signal Tasks.  We see that the average accuracy increases in the former and is relatively static in the latter. This was a criteria for excluding them from the High Signal Task set.
-![accuracy_HS_vs_excluded_tasks_350b_no_stdev_v2.png](accuracy_HS_vs_excluded_tasks_350b_no_stdev_v2.png)
                                Figure 5 :   High-Signal Tasks show increasing accuracy with more training
 The high signal tasks also show lower coefficient of variation  compared to the excluded tasks as shown in Figure 6.  The coefficient of variation is calculated as the ratio between the standard deviation of the average score divided by the mean, where statistics are computed across three random training seeds. Lower coefficient of variation shows more stable results, due to lower variance across random seeds. Their lower coefficient of variation makes the high-signal tasks more reliable at the ablation scale.
-![coeff_variation_HS_vs_excluded_v2.png](coeff_variation_HS_vs_excluded_v2.png)
  Figure  6: Coefficient of Variation (standard deviation divided by mean) for High-Signal Set and Excluded Set.
 &nbsp;&nbsp;**Evaluation Results**
-![fig7.jpg](fig7.jpg)
 **Combining GneissWeb Components into a Winning Recipe**
 There are various ways to combine the key ingredients and build a recipe, including deciding which components to include and their order as well as designing ensemble filtering rules using multiple quality annotators. We performed rigorous ablations by combining the key ingredients in multiple variations and sequences with the aim of maximizing downstream task performance under the constraint of retaining at least 10T tokens from FineWeb.V1.1.0.
-![Ingredients.png](Ingredients.png)
 Figure 19 : Key ingredients selected for building the GneissWeb recipe

 These differences between the High-Signal Tasks vs Extended Tasks are seen in Fig 5 where we see a comparison of the High Signal Tasks vs those which are in the Extended Tasks and excluded from the High Signal Tasks.  We see that the average accuracy increases in the former and is relatively static in the latter. This was a criteria for excluding them from the High Signal Task set.
+<img src="accuracy_HS_vs_excluded_tasks_350b_no_stdev_v2.png" alt="accuracy_HS_vs_excluded_tasks_350b_no_stdev_v2.png" style="width:1000px;"/>
                                Figure 5 :   High-Signal Tasks show increasing accuracy with more training
 The high signal tasks also show lower coefficient of variation  compared to the excluded tasks as shown in Figure 6.  The coefficient of variation is calculated as the ratio between the standard deviation of the average score divided by the mean, where statistics are computed across three random training seeds. Lower coefficient of variation shows more stable results, due to lower variance across random seeds. Their lower coefficient of variation makes the high-signal tasks more reliable at the ablation scale.
+<img src="coeff_variation_HS_vs_excluded_v2.png" alt="coeff_variation_HS_vs_excluded_v2.png" style="width:1000px;"/>
  Figure  6: Coefficient of Variation (standard deviation divided by mean) for High-Signal Set and Excluded Set.
 &nbsp;&nbsp;**Evaluation Results**
+<img src="fig7.jpg" alt="fig7.jpg" style="width:1000px;"/>
 **Combining GneissWeb Components into a Winning Recipe**
 There are various ways to combine the key ingredients and build a recipe, including deciding which components to include and their order as well as designing ensemble filtering rules using multiple quality annotators. We performed rigorous ablations by combining the key ingredients in multiple variations and sequences with the aim of maximizing downstream task performance under the constraint of retaining at least 10T tokens from FineWeb.V1.1.0.
+![](Ingredients.png)
+<img src="Ingredients.png" alt="Ingredients.png" style="width:1000px;"/>
 Figure 19 : Key ingredients selected for building the GneissWeb recipe