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base model = beomi-Llama-3-Open-Ko-8B-Instruct-preview base model = hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview (Trained via Axolotl)
dora_train config (from fsdp_qlora repo)
export CUDA_VISIBLE_DEVICES=0,1
python train.py \
--train_type bnb_dora \
--model_name sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview \
--dataset orca_math \
--dataset_samples 193789 \
--batch_size 4 \
--context_length 8192 \
--gradient_accumulation_steps 2 \
--sharding_strategy full_shard \
--use_gradient_checkpointing true \
--reentrant_checkpointing true \
--use_cpu_offload false \
--use_activation_cpu_offload false \
--log_to wandb \
--project_name "sosoai-fsdp-quantized-ft-exps" \
--save_model true \
--output_dir models/llama-8b-orca-math-10k-bnb-QDoRA
Dataset = hansoldeco domain own dataset (Non open) Dataset = kuotient/orca-math-word-problems-193k-korean
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