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--- |
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datasets: |
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- anon8231489123/ShareGPT_Vicuna_unfiltered |
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language: |
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- zh |
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- en |
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--- |
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*TODO:Upload pending, training is finished. still testing. |
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*Update: Having a bit issue with the tokenizer, still figuring things out. |
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Reproduce Vicuna, but based on yi-6B. The training data I used was ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json. |
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Hyper parameters: |
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``` |
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CUDA_VISIBLE_DEVICES=0,1,2,3,5 torchrun --nproc_per_node 5 ../supervised_finetuning.py \ |
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--model_type auto \ |
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--model_name_or_path /data/llm/models/Pretrained/yi-6B/01ai/Yi-6B \ |
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--tokenizer_name_or_path /data/llm/models/Pretrained/yi-6B/01ai/Yi-6B \ |
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--train_file_dir ../data/finetune/vicuna/ \ |
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--per_device_train_batch_size 2\ |
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--do_train \ |
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--max_train_samples -1 \ |
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--num_train_epochs 3 \ |
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--learning_rate 2e-5 \ |
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--weight_decay 0. \ |
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--bf16 \ |
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--use_peft False \ |
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--logging_strategy steps \ |
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--logging_steps 10 \ |
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--save_strategy epoch \ |
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--save_total_limit 5 \ |
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--gradient_accumulation_steps 1 \ |
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--preprocessing_num_workers 8 \ |
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--output_dir ../outputs/20240106_yi6B_vicuna \ |
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--overwrite_output_dir \ |
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--ddp_timeout 30000 \ |
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--logging_first_step True \ |
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--torch_dtype bfloat16 \ |
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--device_map auto \ |
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--report_to tensorboard \ |
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--ddp_find_unused_parameters False \ |
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--gradient_checkpointing True \ |
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--cache_dir ./cache \ |
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--model_max_length 4096 \ |
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--deepspeed ../deepspeed_zero_stage2_config_no16.json \ |
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--template_name yi |
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``` |
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The training used 5*A800 for 3 epochs |
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``` |
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***** train metrics ***** |
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epoch = 3.0 |
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train_loss = 0.3785 |
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train_runtime = 1 day, 10:01:13.95 |
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train_samples = 93204 |
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train_samples_per_second = 2.24 |
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train_steps_per_second = 0.224 |
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``` |
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We can see from some preliminary results, the conversation is natural and informative (unsurprisingly), also we observe the unfiltering seems to be working! |
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**Heads up** some examples are unsafe and inappropriate, this is entirely for the purpose of testing how un-aligned SFT data affect LLM's final output. |
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