base_model: mistralai/Mixtral-8x7B-v0.1 | |
model_type: AutoModelForCausalLM | |
tokenizer_type: LlamaTokenizer | |
trust_remote_code: true | |
load_in_8bit: false | |
load_in_4bit: true | |
strict: false | |
datasets: | |
- path: tatsu-lab/alpaca | |
type: alpaca | |
dataset_prepared_path: last_run_prepared | |
val_set_size: 0.0 | |
output_dir: ./qlora-out | |
## You can optionally freeze the entire model and unfreeze a subset of parameters | |
unfrozen_parameters: | |
# - lm_head.* | |
# - model.embed_tokens.* | |
# - model.layers.2[0-9]+.block_sparse_moe.gate.* | |
# - model.layers.2[0-9]+.block_sparse_moe.experts.* | |
# - model.layers.3[0-9]+.block_sparse_moe.gate.* | |
# - model.layers.3[0-9]+.block_sparse_moe.experts.* | |
model_config: | |
output_router_logits: true | |
adapter: qlora | |
lora_model_dir: | |
sequence_len: 4096 | |
sample_packing: true | |
pad_to_sequence_len: true | |
lora_r: 32 | |
lora_alpha: 16 | |
lora_dropout: 0.05 | |
lora_target_linear: true | |
lora_fan_in_fan_out: | |
#lora_target_modules: | |
# - gate | |
# - q_proj | |
# - k_proj | |
# - v_proj | |
# - o_proj | |
# - w1 | |
# - w2 | |
# - w3 | |
wandb_project: | |
wandb_entity: | |
wandb_watch: | |
wandb_name: | |
wandb_log_model: | |
gradient_accumulation_steps: 2 | |
micro_batch_size: 1 | |
num_epochs: 1 | |
optimizer: adamw_bnb_8bit | |
lr_scheduler: cosine | |
learning_rate: 0.0002 | |
train_on_inputs: false | |
group_by_length: false | |
bf16: auto | |
fp16: | |
tf32: false | |
gradient_checkpointing: true | |
early_stopping_patience: | |
resume_from_checkpoint: | |
local_rank: | |
logging_steps: 1 | |
xformers_attention: | |
flash_attention: true | |
loss_watchdog_threshold: 5.0 | |
loss_watchdog_patience: 3 | |
warmup_steps: 10 | |
evals_per_epoch: 4 | |
eval_table_size: | |
eval_max_new_tokens: 128 | |
saves_per_epoch: 1 | |
debug: | |
deepspeed: deepspeed_configs/zero2.json | |
weight_decay: 0.0 | |
fsdp: | |
fsdp_config: | |
special_tokens: | |