Domain Effectiveness Optimization Program
The following combination of methods can be used to address the special assessment indicator enhancement:
- Benchmark Test Positioning::
first runevaluate.py --model <path> --benchmark全部Generate complete assessment reports that identify areas of weakness (e.g., code/math) - data enhancement::
To weak areas:- utilization
generate.py --task_type代码Generation of specialized data - Download domain datasets from Hugging Face Hub (e.g. BigCode's The Stack)
- utilization
- Training Strategy Adjustment::
In multi_stage_training.py:- Increase domain data batch ratio (-domain_ratio)
- Extend the number of training steps for the domain (-domain_steps)
- Use domain adaptive learning rate (-domain_lr)
- model fusion::
to the final output model:- Merge multiple domain expert models using checkpoint-ensemble technique
- Optimization of fusion weights by hyperparametric scanning via wandb
Recommended after each round of optimization--benchmark单一领域parameter to quickly verify the effect.
This answer comes from the articleOpen R1: Hugging Face Replicates the Training Process of DeepSeek-R1The
May not be reproduced without permission:AI productivity tools " How can we improve the assessment of Open R1 models in specific domains?
