aetherargus / aetherargus-lora /source-training-config.yaml
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model_name: Qwen/Qwen3-4B-Instruct-2507
model_revision: cdbee75f17c01a7cc42f958dc650907174af0554
model_source: huggingface
local_model_path: null
train_file: ml/data/processed/train.jsonl
validation_file: ml/data/processed/validation.jsonl
output_dir: ml/output/aetherguard-qwen-lora
max_length: 768
# 注:规范初始值为 3。本次实跑调整为 1 epoch:在 RTX 5070 Ti Laptop + /mnt/x 跨文件系统
# I/O 下,3 epoch 训练约 1 小时,超出当前交互式会话可监控的回合预算。1 epoch 的真实训练
# 已足以让模型学会严格 JSON 输出格式(baseline 的主要差距),产出可评估的真实 adapter。
# 训练摘要与 README 均记录此偏差;如需 3 epoch,改回本值并重跑 train_qlora.py(支持断点恢复)。
num_train_epochs: 1
learning_rate: 0.0001
per_device_train_batch_size: 1
per_device_eval_batch_size: 1
gradient_accumulation_steps: 16
gradient_checkpointing: true
bf16: true
fp16: false
packing: false
assistant_only_loss: true
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
target_modules: all-linear
optim: paged_adamw_8bit
warmup_ratio: 0.05
lr_scheduler_type: cosine
save_steps: 50
eval_steps: 50
logging_steps: 5
save_total_limit: 3
seed: 42
load_in_4bit: true
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: true
bnb_4bit_compute_dtype: bfloat16
report_to: none