Update README.md

README.md

@@ -7,7 +7,7 @@ library_name: transformers
 pipeline_tag: text-generation
 ---
 
-![SauerkrautLM](images/SauerkrautLM.png "SauerkrautLM")
+![SauerkrautLM](images/hero-multi.png "SauerkrautLM-7b-HerO-multilingual")
 ## VAGO solutions SauerkrautLM
 Introducing SauerkrautLM-v1 - Your German Language Powerhouse! 
 
@@ -37,9 +37,9 @@ Data augmentation techniques were used to grant grammatical, syntactical correct
 
 **Merge Procedure:**
 
-SauerkrautLM-7b-HerO was merged on 1 A100 with mergekit.
-The merged model contains [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
-We used the gradient slurp technique.
+SauerkrautLM-7b-HerO was merged on 1 A100 with [mergekit](https://github.com/cg123/mergekit).
+The merged model contains [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca).
+We used the gradient SLURP method.
 
 
 - **Model Type:** SauerkrautLM-7b-HerO is an auto-regressive language model based on the transformer architecture 
@@ -111,8 +111,33 @@ Please tell me about how merged models can benefit from existent top-models.<|im
 |                    |       |ter     | 0.6463|±  |0.0039|
 |xnli_de             |      0|acc     | 0.4547|±  |0.0070|
 |xnli_en             |      0|acc     | 0.5595|±  |0.0070|
+```
+**BBH**
+```  
+|                      Task                      |Version|       Metric        |Value |   |Stderr|
+|------------------------------------------------|------:|---------------------|-----:|---|-----:|
+|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.6053|±  |0.0356|
+|bigbench_date_understanding                     |      0|multiple_choice_grade|0.6992|±  |0.0239|
+|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.3721|±  |0.0302|
+|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.1671|±  |0.0197|
+|                                                |       |exact_str_match      |0.1003|±  |0.0159|
+|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2540|±  |0.0195|
+|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.2043|±  |0.0152|
+|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.4667|±  |0.0289|
+|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.3700|±  |0.0216|
+|bigbench_navigate                               |      0|multiple_choice_grade|0.4970|±  |0.0158|
+|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.6965|±  |0.0103|
+|bigbench_ruin_names                             |      0|multiple_choice_grade|0.4152|±  |0.0233|
+|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.1443|±  |0.0111|
+|bigbench_snarks                                 |      0|multiple_choice_grade|0.6464|±  |0.0356|
+|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.6846|±  |0.0148|
+|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.3150|±  |0.0147|
+|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2168|±  |0.0117|
+|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1537|±  |0.0086|
+|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4667|±  |0.0289|
 ```  
 
+
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