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tuantmdev/
3dcb29bc-8dff-4a33-ab5c-4bcd8b2ca87b

Name: 3dcb29bc-8dff-4a33-ab5c-4bcd8b2ca87b
Availability: InStock

generated_from_trainer

elyza/Llama-3-ELYZA-JP-8B

adapter

llama3

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
auto_find_batch_size: true
base_model: elyza/Llama-3-ELYZA-JP-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 5e3277732f6641a2_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/5e3277732f6641a2_train_data.json
  type:
    field_input: keyword
    field_instruction: problem
    field_output: outputs
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: true
hub_model_id: tuantmdev/3dcb29bc-8dff-4a33-ab5c-4bcd8b2ca87b
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 1e-4
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 40
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 400
micro_batch_size: 2
mlflow_experiment_name: /tmp/5e3277732f6641a2_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
save_strategy: steps
sequence_len: 512
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: 5003b58d-7b70-42b3-aa07-ea56bbef0106
wandb_project: Gradients-On-Demand
wandb_run: unknown
wandb_runid: 5003b58d-7b70-42b3-aa07-ea56bbef0106
warmup_steps: 80
weight_decay: 0.0
xformers_attention: null

3dcb29bc-8dff-4a33-ab5c-4bcd8b2ca87b

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: 0.6805

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: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
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: 80
training_steps: 400

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0001	1	1.2341
1.0538	0.0061	50	0.8957
0.8832	0.0122	100	0.8020
0.7876	0.0183	150	0.7560
0.7641	0.0245	200	0.7258
0.7027	0.0306	250	0.7050
0.7026	0.0367	300	0.6899
0.6955	0.0428	350	0.6821
0.6919	0.0489	400	0.6805

Framework versions

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