Comments (4)
Thanks for your interest!
Below are some questions for me to know what is going on:
- What codebase are you using for sft?
- Can you specifically let me know your training setting?
- How much exactly is the slight performance gap?
- I don't think we showcase the performance for llama2 fine-tuning on cherry_data_v1, so which list of performance are you comparing your model with?
- Indeed previously there was one bro using the incorrect scripts. Can you send me your scripts used in lm-evaluation-harness for evaluation?
As for "data splits in cherry_data_v1 are not exactly 5%, 10%, or 15%", yes indeed. It is caused by our previous filtering mechanism. Thus in our paper, we all present "approximately 5% data" or so and present the specific data number in the paper.
from cherry_llm.
Thx for your quick reply.
- I used llama-factory for sft.
- I train these three models under the same settings as yours:
batch_size 128, learning_rate 2e-5, num_train_epochs 3, warmup_ratio 0.03, max_length 2048
. Due to hardware limitations, I use fp16 rather than bf16 in my training. - As for the performance gap, please check the attached files below.
cherry_5percent.json
cherry_10percent.json
cherry_15percent.json - I'm comparing mine with the results you report here.
- I used
CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch --main_process_port 29999 -m lm_eval --model hf --model_args pretrained={sft_model_path} --tasks mmlu,ai2_arc,hellaswag,truthfulqa --batch_size 1 --output_path {log_path}
in 'lm-evaluation-harness' for evaluation.
from cherry_llm.
Thanks for your reply~
- Since you are using a different codebase, so there is a great possibility that different prompts will lead to a different performance when using lm-evaluation-harness, as they don't support customized prompts. As mentioned, for llama2 models, we used vicuna prompt for training.
- I think the settings are the same as ours.
- N/A
- We are sorry for the misunderstanding. In this table, they are not using cherry_data_v1 (calculated based on llama1), but the "IFD scores are calculated on llama2-7b or llama2-13b". So there should be gaps if using cherry_data_v1 data.
Also, the "IFD scores are calculated on llama2-7b or llama2-13b" is also released recently, please check: Alpaca llama2 7b, Alpaca llama2 13b, WizardLM70k llama2 7b, WizardLM70k llama2 13b.
You might need to sort the data by yourself. - It seems that all of the testing scripts you are using are Zeroshot, however, according to the open_llm_leaderboard site, most of them are using the few-shot. So I think this is the main reason.
To conclude, the main reason is that you are not using the few-shot settings mentioned in the open_llm_leaderboard. Besides, it's better to use "IFD scores are calculated on llama2-7b or llama2-13b".
from cherry_llm.
ic, I'll try this later. Thx again.
from cherry_llm.
Related Issues (19)
- Need help: the loss curve is strange. HOT 3
- 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
- how many epochs to train on cherry data? HOT 2
- a confusion about Instruction-Following Difficulty (IFD) scores HOT 2
- a confusion about data_by_IFD HOT 3
- Logic behind IFD score HOT 1
- I plan to apply this method on Llama2, which part of this project needs to be changed to adapt to Llama2? 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
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