See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 989ae4d0cc890f33_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/989ae4d0cc890f33_train_data.json
  type:
    field_instruction: ja
    field_output: en
    format: '{instruction}'
    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/b448dbc0-2d81-4ed9-a8fc-a741f4a6f15a
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: 1092
micro_batch_size: 4
mlflow_experiment_name: /tmp/989ae4d0cc890f33_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: 0.03399602926378199
wandb_entity: null
wandb_mode: online
wandb_name: 423cd34d-e3d9-4c7c-8704-08a2490a5ff1
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 423cd34d-e3d9-4c7c-8704-08a2490a5ff1
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

b448dbc0-2d81-4ed9-a8fc-a741f4a6f15a

This model is a fine-tuned version of MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6989

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: 1092

Training results

Training Loss	Epoch	Step	Validation Loss
2.719	0.0002	1	2.6623
0.9509	0.0225	100	0.9122
0.9781	0.0450	200	0.9206
0.8212	0.0676	300	0.9016
0.7889	0.0901	400	0.8712
0.9269	0.1126	500	0.8423
0.7262	0.1351	600	0.8063
0.6935	0.1577	700	0.7689
0.6466	0.1802	800	0.7353
0.7333	0.2027	900	0.7096
0.764	0.2252	1000	0.6989

Framework versions

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