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tryingpro/
fd8f2607-c269-4f29-8e4d-a6f0c162bd1e

Name: fd8f2607-c269-4f29-8e4d-a6f0c162bd1e
Availability: InStock

generated_from_trainer

MLP-KTLim/llama-3-Korean-Bllossom-8B

adapter

llama3

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 60e835c3614bd368_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/60e835c3614bd368_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: tryingpro/fd8f2607-c269-4f29-8e4d-a6f0c162bd1e
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: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 2
max_steps: 90
micro_batch_size: 2
mlflow_experiment_name: /tmp/60e835c3614bd368_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1.0e-05
optimizer: adamw_torch
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: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: tryingpro-unicourt
wandb_mode: online
wandb_name: 0d69a911-2d92-478a-b097-7e7c31bb623e
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0d69a911-2d92-478a-b097-7e7c31bb623e
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

fd8f2607-c269-4f29-8e4d-a6f0c162bd1e

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: 1.1330

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: 32
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
training_steps: 90

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0001	1	1.5589
1.4465	0.0010	8	1.4427
1.283	0.0021	16	1.2521
1.1811	0.0031	24	1.1970
1.1976	0.0042	32	1.1761
1.1093	0.0052	40	1.1608
1.1565	0.0062	48	1.1503
1.1912	0.0073	56	1.1443
1.1902	0.0083	64	1.1397
1.1386	0.0093	72	1.1357
1.1285	0.0104	80	1.1336
1.11	0.0114	88	1.1330

Framework versions

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