base_model: kakaocorp/kanana-1.5-15.7b-a3b-instruct
hf_cache_dir: ../../../../data5/models

load_in_8bit: false
load_in_4bit: true

datasets:
  - path: data_cap_50.jsonl
    type: chat_template
    split: train

dataset_prepared_path: preprocess
val_set_size: 0
output_dir: ./outputs-no_unique-15_7b
dataloader_num_workers: 32

sequence_len: 5120
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

wandb_project: TTP_SFT_LLM_RE
wandb_entity:
wandb_watch:
wandb_name: ttp_sft_kanana-1.5_base_unique_remove-15_7b
wandb_log_model:
hub_model_id: CaffeineThief/ttp_sft_kanana-1.5_base_unique_remove-15_7b
hub_private_repo: false

gradient_accumulation_steps: 3
micro_batch_size: 16
num_epochs: 2
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-5

bf16: auto
tf32: false

gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
weight_decay: 0.01
# evals_per_epoch: 1
saves_per_epoch: 0

ddp: false

# fsdp:
#   - full_shard
#   - auto_wrap
# fsdp_config:
#   fsdp_state_dict_type: FULL_STATE_DICT
#   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
#   fsdp_activation_checkpointing: true
#   transformer_layer_cls_to_wrap: MixtralDecoderLayer

ttp_sft_kanana-1.5_base_unique_remove-15_7b

This model is a fine-tuned version of kakaocorp/kanana-1.5-15.7b-a3b-instruct on the data_cap_50.jsonl dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 3
total_train_batch_size: 48
optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 4
training_steps: 99

Training results

Framework versions

PEFT 0.18.1
Transformers 4.57.6
Pytorch 2.8.0+cu128
Datasets 4.3.0
Tokenizers 0.22.2