---
library_name: transformers
license: apache-2.0
base_model: kakaocorp/kanana-1.5-2.1b-instruct-2505
tags:
- axolotl
- generated_from_trainer
datasets:
- tram2_train_step4_re.jsonl
model-index:
- name: ttp_sft_kanana-1.5_steps_tram2_step4
  results: []
---

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should probably proofread and complete it, then remove this comment. -->

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<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: tram2_train_step4_re.jsonl
    type: chat_template
    split: train

dataset_prepared_path: preprocess
val_set_size: 0
output_dir: ./outputs-kanana-steps-tram2-step4
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_steps_tram2_step4
wandb_log_model:
hub_model_id: CaffeineThief/ttp_sft_kanana-1.5_steps_tram2_step4
hub_private_repo: false

gradient_accumulation_steps: 1
micro_batch_size: 16
num_epochs: 3
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>

# ttp_sft_kanana-1.5_steps_tram2_step4

This model is a fine-tuned version of [kakaocorp/kanana-1.5-2.1b-instruct-2505](https://huggingface.co/kakaocorp/kanana-1.5-2.1b-instruct-2505) on the tram2_train_step4_re.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
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 48
- total_eval_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: 10
- training_steps: 204

### Training results



### Framework versions

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