See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4
bf16: false
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - e6ade7e8b73765fc_train_data.json
  ds_type: json
  field: prompt
  path: /workspace/input_data/e6ade7e8b73765fc_train_data.json
  type: completion
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
ema_decay: 0.9992
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: JoshMe1/a4882631-98d2-40fc-82f8-25b7c850871c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 0.3
max_memory:
  0: 130GB
max_steps: 500
micro_batch_size: 2
mlflow_experiment_name: /tmp/e6ade7e8b73765fc_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_hf
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
use_ema: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 795c5692-e6b6-4286-bc7f-0bcd6ab00dc4
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 795c5692-e6b6-4286-bc7f-0bcd6ab00dc4
warmup_ratio: 0.03
weight_decay: 0.01
xformers_attention: null

a4882631-98d2-40fc-82f8-25b7c850871c

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: 1.6356

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: 5e-06
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_HF 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: 15
• training_steps: 500
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0001	1	2.1328
1.703	0.0113	100	1.7244
1.6673	0.0226	200	1.6646
1.6397	0.0339	300	1.6442
1.6283	0.0453	400	1.6368
1.6255	0.0566	500	1.6356

Framework versions

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