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