See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: elyza/Llama-3-ELYZA-JP-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 55fc4b709c64c233_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/55fc4b709c64c233_train_data.json
  type:
    field_input: hypothesis
    field_instruction: premise
    field_output: label
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: dimasik87/06770fdd-0810-4759-908a-5d296333672a
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: 1
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: 70GiB
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/55fc4b709c64c233_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 4
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
special_tokens:
  pad_token: <|eot_id|>
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: 06770fdd-0810-4759-908a-5d296333672a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 06770fdd-0810-4759-908a-5d296333672a
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

06770fdd-0810-4759-908a-5d296333672a

This model is a fine-tuned version of elyza/Llama-3-ELYZA-JP-8B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5962

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_BNB 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss
12.2382	0.0009	1	12.7834
12.0138	0.0034	4	10.4872
4.8259	0.0069	8	1.5156
0.5719	0.0103	12	0.8780
0.9423	0.0138	16	0.5733
0.4543	0.0172	20	0.8335
1.3692	0.0207	24	1.0001
0.9116	0.0241	28	0.6467
0.7579	0.0276	32	0.8243
0.4857	0.0310	36	0.6007
0.723	0.0345	40	0.6944
0.3938	0.0379	44	0.6142
0.4404	0.0414	48	0.5962

Framework versions

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