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axolotl/examples/mistral4/qlora-text.yml
2026-03-20 17:11:46 +07:00

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YAML

base_model: axolotl-ai-co/Mistral-Small-4-119B-2603-BF16
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
load_in_4bit: true
quantize_moe_experts: true
datasets:
- path: fozziethebeat/alpaca_messages_2k_test
type: chat_template
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./outputs/out
adapter: qlora
sequence_len: 2048
sample_packing: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
# uncomment to train on expert layers
# lora_target_parameters:
# - mlp.experts.gate_up_proj
# - mlp.experts.down_proj
# lora_mlp_kernel: false
# lora_qkv_kernel: false
# lora_o_kernel: false
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: true
tf32: true
gradient_checkpointing: true
logging_steps: 1
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
weight_decay: 0.0