Update README.md

README.md

@@ -11,30 +11,6 @@ Curating the best open reasoning datasets.  A [Bespoke Labs](https://bespokelabs
 
 Our first goal is to curate a reasoning dataset to train state of the art small reasoning models that surpass [DeepSeek-R1-Distill-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) and [DeepSeek-R1-Distill-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) on math and code reasoning benchmarks.
 
-## Latest Results
-
-<Table
-  data={{
-    headers: [
-      "Model",
-      "AIME24",
-      "MATH500",
-      "GPQA-D",
-      "LCB Easy",
-      "LCB Med",
-      "LCB Hard"
-    ],
-    rows: [
-      ["Open-Thinker-7B", "26.7", "86", "40.4", "74.2", "31.1", "3.3"],
-      ["Bespoke-Stratos-7B", "20.0", "82.0", "37.8", "71.4", "25.2", "1.6"],
-      ["DeepSeek-R1-Distill-Qwen-7B", "43.3", "89.4", "44.9", "81.3", "42.2", "2.4"],
-      ["gpt-4o-0513", "9.3", "74.6", "49.9", "-", "-", "-"],
-      ["o1-mini", "63.6", "90.0", "60.0", "-", "-", "-"]
-    ]
-  }}
-/>
-
-
 ## About us
 
 We are a team of researchers and engineers from Bespoke Labs, Stanford, University of California Berkeley, University of Washington, Juelich Supercomputing Center (JSC), LAION, UCLA, UNC Chapel Hill, and Toyota Research Institute united around building the best datasets (and thus the best models). See our previous works at [datacomp.ai](https://www.datacomp.ai/) and [mlfoundations](https://github.com/mlfoundations).