See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - f9aa7c838f7cdf63_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/f9aa7c838f7cdf63_train_data.json
  type:
    field_instruction: da
    field_output: da_bornholm
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: dimasik1987/af3d5b0b-2cbf-4179-90fb-863d9c446340
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 78GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/f9aa7c838f7cdf63_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 6d3d3ee9-0155-4743-862e-7d03a5851a49
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6d3d3ee9-0155-4743-862e-7d03a5851a49
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true

af3d5b0b-2cbf-4179-90fb-863d9c446340

This model is a fine-tuned version of MLP-KTLim/llama-3-Korean-Bllossom-8B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.8100

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: 0.0002
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
• training_steps: 30

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0013	1	7.3274
6.9062	0.0064	5	6.2367
5.0131	0.0128	10	3.6506
3.3779	0.0192	15	3.1772
3.6967	0.0256	20	2.9306
2.7301	0.0320	25	2.8277
3.7413	0.0384	30	2.8100

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.5.0+cu124
• Datasets 3.0.1
• Tokenizers 0.20.1