Update README.md

README.md

@@ -64,31 +64,7 @@ print(f"Prediction: {'Correct' if score > 0.5 else 'Incorrect'}")
 
 ## Training Details
 
-This model was trained using the [Weaver distillation pipeline](https://github.com/ScalingIntelligence/scaling-verification/tree/main/distillation) on a combined dataset spanning multiple reasoning domains. For training your own distilled models, see the [distillation README](https://github.com/ScalingIntelligence/scaling-verification/blob/main/distillation/README.md).
-
-## Evaluation
-
-Evaluate this model on different datasets:
-
-```bash
-# MATH500
-python evaluate_crossencoder.py \
-  --model_name "Alibaba-NLP/gte-Qwen2-1.5B-instruct" \
-  --checkpoint_path "hazyresearch/Weaver_Distilled_All_Datasets_gte-Qwen2-1.5B-instruct" \
-  --dataset_path "hazyresearch/MATH500_with_Llama_3.1_70B_Instruct_v1" \
-  --dataset_split "data" \
-  --max_length 4096 \
-  --batch_size 64
-
-# GPQA
-python evaluate_crossencoder.py \
-  --model_name "Alibaba-NLP/gte-Qwen2-1.5B-instruct" \
-  --checkpoint_path "hazyresearch/Weaver_Distilled_All_Datasets_gte-Qwen2-1.5B-instruct" \
-  --dataset_path "hazyresearch/GPQA_with_Llama_3.1_70B_Instruct_v1" \
-  --dataset_split "data" \
-  --max_length 4096 \
-  --batch_size 64
-```
+This model was trained using the [Weaver distillation pipeline](https://github.com/HazyResearch/scaling-verification) on a combined dataset spanning multiple reasoning domains. For training your own distilled models, see the [distillation README](https://github.com/HazyResearch/scaling-verification/blob/main/distillation/README.md).
 
 ## Citation