test: fix gemma GSM8K thresholds in nightly text eval (#27342)

test/registered/eval/test_text_models_gsm8k_eval.py

@@ -30,7 +30,7 @@ MODEL_SCORE_THRESHOLDS = {
     "meta-llama/Llama-3.1-8B-Instruct": 0.80,  # 84.5% - 5%
     "mistralai/Mistral-7B-Instruct-v0.3": 0.47,  # 52.1% - 5%
     "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct": 0.81,  # 86.4% - 5%
-    "google/gemma-2-27b-it": 0.86,  # 90.7% - 5%
+    "google/gemma-2-27b-it": 0.81,  # 85.5% measured - 5%
     "meta-llama/Llama-3.1-70B-Instruct": 0.89,  # 94.1% - 5%
     "mistralai/Mixtral-8x7B-Instruct-v0.1": 0.69,  # 74.4% - 5%
     "Qwen/Qwen2-57B-A14B-Instruct": 0.76,  # 80.7% - 5% (official A14B score; 88.2% was the 72B)
@@ -38,9 +38,7 @@ MODEL_SCORE_THRESHOLDS = {
     "neuralmagic/Mistral-7B-Instruct-v0.3-FP8": 0.47,  # 52.1% - 5%
     "neuralmagic/DeepSeek-Coder-V2-Lite-Instruct-FP8": 0.81,  # 86.4% - 5%
     "zai-org/GLM-4.5-Air-FP8": 0.80,  # ~85%  - 5%
-    # GSM8K baseline for gemma-2-2b is ~40-45%; threshold set at 5% below.
-    # (Previously 0.50 based on MGSM-EN; tracked regression: https://github.com/sgl-project/sglang/issues/4324)
-    "neuralmagic/gemma-2-2b-it-FP8": 0.38,  # ~43%  - 5%
+    "neuralmagic/gemma-2-2b-it-FP8": 0.53,  # 58.4% measured - 5%
     "neuralmagic/Meta-Llama-3.1-70B-Instruct-FP8": 0.89,  # 94.1% - 5%
     "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8": 0.69,  # 74.4% - 5%
     "neuralmagic/Qwen2-72B-Instruct-FP8": 0.86,  # 91.1% - 5%