Comments (1)
Thank you for your interest in our work~
We have done experiments on llama2 on Alpaca data without using the pre-experienced model for simplicity. It works fine, which shows the effectiveness of our approach. We also got the IFD scores of wizardLM data based on llama2 but haven't had time to train them. We will release all these data and model performances soon in the next version.
I don't think there is much modification that should be made to adapt to llama2. You can directly use it.
I think there are indeed some things that can be further explored like the use of pre-trained models, or the use of different prompt templates or so.
So please feel free to explore what you like~
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Related Issues (20)
- a confusion about Instruction-Following Difficulty (IFD) scores HOT 2
- a confusion about data_by_IFD HOT 3
- Logic behind IFD score HOT 1
- May I ask if this project is suitable for other large models, such as the Baichuan model, to filter high-quality datasets from other fields HOT 4
- about the paper HOT 1
- Multi-round conversation data set HOT 3
- GPT-4/ChatGPT Evaluation Code HOT 1
- How to filter code SFT data? HOT 2
- Questions related to training HOT 5
- Could the Pre-Experienced Model be used in other different dataset? HOT 1
- Any report of time consuming? HOT 1
- Chinese SFT data cannot be displayed. HOT 3
- 'The training of pre-experienced models is discarded for more efficient usage': that means we can only use base model to do cherry analysis and selection? HOT 1
- batch? HOT 1
- 关于Direct Answer Score sθ(A) HOT 2
- Evaluation reproducibility on benchmarks HOT 4
- how many epochs to train on cherry data? HOT 2
- Question about the effect of labels[0, :start_token] = -100 HOT 1
- why is the process so slow HOT 5
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