update readme

README.md

@@ -55,23 +55,26 @@ python eval_multigpu.py \
 This script calculates the scores for each expert based on the evaluation datasets.
 **Usage:**
 ```bash
+export PYTHONPATH=$PYTHONPATH:$(pwd)
 python scripts/expert/get_expert_scores.py \
-    --eval_dataset=translation \
+    --eval_dataset=intent \
     --base_model_path=deepseek-ai/ESFT-vanilla-lite \
-    --output_dir=results/expert_scores/translation \
+    --output_dir=results/expert_scores/intent \
     --n_sample_tokens=131072 \
     --world_size=4 \
     --gpus_per_rank=2
+    # for N gpus, world_size should be N / gpus_per_rank
 ```
 
 3. **generate_expert_config.py**
 This script generates the configuration to convert a MoE model with only task-relevant tasks trained based on evaluation scores.
 **Usage:**
 ```bash
+export PYTHONPATH=$PYTHONPATH:$(pwd)
 python scripts/expert/generate_expert_config.py \
-    --eval_datasets=intent,summary,law,translation \
-    --expert_scores_dir=results/expert_scores \
-    --output_dir=results/expert_configs \
+    --eval_dataset=intent \
+    --expert_scores_dir=results/expert_scores/intent \
+    --output_path=results/expert_configs/intent.json \
     --score_function=token \
     --top_p=0.2 # the scoring function and top_p are hyperparameters
 ```