See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: unsloth/gemma-2b-it
bf16: auto
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
  - 6755b140e3dbc3a5_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/6755b140e3dbc3a5_train_data.json
  type:
    field_instruction: article
    field_output: ingress
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: '{'''':torch.cuda.current_device()}'
do_eval: true
early_stopping_patience: 1
eval_batch_size: 1
eval_sample_packing: false
eval_steps: 25
evaluation_strategy: steps
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 64
gradient_checkpointing: true
group_by_length: true
hub_model_id: sn56/a3aef118-d5c4-4a66-9cad-1c6576184a25
hub_repo: stevemonite
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 70GiB
max_steps: 1600
micro_batch_size: 1
mlflow_experiment_name: /tmp/6755b140e3dbc3a5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
optimizer: adamw_torch
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: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: false
train_on_inputs: false
trust_remote_code: true
val_set_size: 50
wandb_entity: sn56-miner
wandb_mode: disabled
wandb_name: a3aef118-d5c4-4a66-9cad-1c6576184a25
wandb_project: god
wandb_run: x1lv
wandb_runid: a3aef118-d5c4-4a66-9cad-1c6576184a25
warmup_raio: 0.03
warmup_ratio: 0.04
weight_decay: 0.01
xformers_attention: null

a3aef118-d5c4-4a66-9cad-1c6576184a25

This model is a fine-tuned version of unsloth/gemma-2b-it on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.8476

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: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 64
• total_train_batch_size: 256
• total_eval_batch_size: 4
• 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-5
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 64
• training_steps: 1600

Training results

Training Loss	Epoch	Step	Validation Loss
3.7609	0.0014	1	3.9861
3.0499	0.0342	25	2.7601
2.4286	0.0683	50	2.3274
2.2835	0.1025	75	2.2020
2.1983	0.1366	100	2.1237
2.1024	0.1708	125	2.0570
2.1827	0.2049	150	2.0219
2.1869	0.2391	175	2.0059
2.0156	0.2733	200	1.9924
2.1717	0.3074	225	1.9814
1.994	0.3416	250	1.9634
2.1325	0.3757	275	1.9347
1.9053	0.4099	300	1.9331
2.0007	0.4440	325	1.9054
1.9177	0.4782	350	1.9128
1.9729	0.5123	375	1.8935
1.9146	0.5465	400	1.8807
1.9364	0.5807	425	1.8594
1.9185	0.6148	450	1.8585
1.8136	0.6490	475	1.8454
1.8548	0.6831	500	1.8400
1.8635	0.7173	525	1.8476

Framework versions

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