Update README.md

README.md

@@ -95,10 +95,23 @@ maximize the potential of all individuals and organizations.
 ### Benchmark Metrics
 
 Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); 
-model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-2.8b` is not state of the art, 
-and in fact underperforms `dolly-v1-6b` in some evaluation benchmarks. We believe this owes to the composition and size of the underlying fine tuning datasets, 
-but a robust statement as to the sources of these variations requires further study.  
+model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-2.8b` is not state of the art. 
+It underperforms `dolly-v1-6b` in the evaluation benchmarks, which is not surprising considering it has half the number of parameters.  
 
-TODO benchmarks
+```
++-----------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------+
+|  model                            |   openbookqa |   arc_easy |   winogrande |   hellaswag |   arc_challenge |     piqa |    boolq |    gmean |
++-----------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------|
+| EleutherAI/pythia-2.8b            |        0.348 |   0.585859 |     0.589582 |    0.591217 |        0.323379 | 0.73395  | 0.638226 | 0.523431 |
+| EleutherAI/pythia-6.9b            |        0.368 |   0.604798 |     0.608524 |    0.631548 |        0.343857 | 0.761153 | 0.6263   | 0.543567 |
+| databricks/dolly-v2-2.8b          |        0.384 |   0.611532 |     0.589582 |    0.650767 |        0.370307 | 0.742655 | 0.575535 | 0.544886 |
+| EleutherAI/pythia-12b             |        0.364 |   0.627104 |     0.636148 |    0.668094 |        0.346416 | 0.760065 | 0.673394 | 0.559676 |
+| EleutherAI/gpt-j-6B               |        0.382 |   0.621633 |     0.651144 |    0.662617 |        0.363481 | 0.761153 | 0.655963 | 0.565936 |
+| databricks/dolly-v2-12b           |        0.408 |   0.63931  |     0.616417 |    0.707927 |        0.388225 | 0.757889 | 0.568196 | 0.56781  |
+| databricks/dolly-v2-6.9b          |        0.392 |   0.633838 |     0.607735 |    0.686517 |        0.406997 | 0.750816 | 0.644037 | 0.573487 |
+| databricks/dolly-v1-6b            |        0.41  |   0.62963  |     0.643252 |    0.676758 |        0.384812 | 0.773667 | 0.687768 | 0.583431 |
+| EleutherAI/gpt-neox-20b           |        0.402 |   0.683923 |     0.656669 |    0.7142   |        0.408703 | 0.784004 | 0.695413 | 0.602236 |
++-----------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------+
+```
 
 # Happy Hacking!