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Alphatao/
dc3a233a-be35-4d55-a70f-b62e75edddfc

generated_from_trainer

oopsung/llama2-7b-koNqa-test-v1

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: oopsung/llama2-7b-koNqa-test-v1
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 59eda594b1666fc6_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/59eda594b1666fc6_train_data.json
  type:
    field_input: phonemes
    field_instruction: text
    field_output: text_description
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/dc3a233a-be35-4d55-a70f-b62e75edddfc
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 974
micro_batch_size: 4
mlflow_experiment_name: /tmp/59eda594b1666fc6_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
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: 100
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.03261684986464007
wandb_entity: null
wandb_mode: online
wandb_name: f68e9a08-3b7c-44f2-877a-73cf5489e1bf
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f68e9a08-3b7c-44f2-877a-73cf5489e1bf
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

dc3a233a-be35-4d55-a70f-b62e75edddfc

This model is a fine-tuned version of oopsung/llama2-7b-koNqa-test-v1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4163

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
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: 974

Training results

Training Loss	Epoch	Step	Validation Loss
4.1155	0.0002	1	4.0413
0.5281	0.0216	100	0.5416
0.445	0.0432	200	0.5027
0.4575	0.0647	300	0.4850
0.4514	0.0863	400	0.4668
0.4655	0.1079	500	0.4536
0.4509	0.1295	600	0.4387
0.3956	0.1510	700	0.4294
0.4479	0.1726	800	0.4209
0.4473	0.1942	900	0.4163

Framework versions

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