Modelpublic

CaffeineThief/ttp_sft_kanana-1.5_annoctr_base_data_cleaned

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face

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Model Card

<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.12.2

yaml

base_model: kakaocorp/kanana-1.5-2.1b-instruct-2505
hf_cache_dir: ../../../../data5/models

load_in_8bit: false
load_in_4bit: false

datasets:
  - path: llm_annoctr_filtered_base_data_cleaned.json
    type: chat_template
    split: train

dataset_prepared_path: preprocess
val_set_size: 0
output_dir: ./outputs-annoctr_base_data_cleaned
dataloader_num_workers: 32

sequence_len: 3072
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

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_annoctr_base_data_cleaned
wandb_log_model:
hub_model_id: CaffeineThief/ttp_sft_kanana-1.5_annoctr_base_data_cleaned
hub_private_repo: false

gradient_accumulation_steps: 1
micro_batch_size: 16
num_epochs: 2
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-5

bf16: auto
tf32: false

gradient_checkpointing: false
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

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

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

</details><br>

ttpsftkanana-1.5annoctrbasedatacleaned

This model is a fine-tuned version of kakaocorp/kanana-1.5-2.1b-instruct-2505 on the llmannoctrfilteredbasedata_cleaned.json 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
—trainbatchsize: 16
—evalbatchsize: 16
—seed: 42
—distributed_type: multi-GPU
—num_devices: 3
—totaltrainbatch_size: 48
—totalevalbatch_size: 48
—optimizer: Use adamwtorchfused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
—lrschedulertype: cosine
—lrschedulerwarmup_steps: 3
—training_steps: 74

Training results

Framework versions

—Transformers 4.55.2
—Pytorch 2.6.0+cu124
—Datasets 4.0.0
—Tokenizers 0.21.4