adapter: lora
base_model: EleutherAI/pythia-14m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - stsbenchmark-sts_train_data.json
  ds_type: json
  path: /workspace/input_data/stsbenchmark-sts_train_data.json
  type:
    field_input: sentence2
    field_instruction: sentence1
    field_output: genre
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 10
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hours_to_complete: 2
hub_model_id: besimray/miner1_c6f8f369-86ff-49b4-9737-598659d9e56b_1731003791
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
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: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 1
mlflow_experiment_name: /tmp/stsbenchmark-sts_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
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: 10
save_strategy: steps
sequence_len: 4096
started_at: '2024-11-07T18:23:11.087868'
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: besimray24-rayon
wandb_mode: online
wandb_project: Public_TuningSN
wandb_run: miner_id_24
wandb_runid: c6f8f369-86ff-49b4-9737-598659d9e56b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

miner1_c6f8f369-86ff-49b4-9737-598659d9e56b_1731003791

This model is a fine-tuned version of EleutherAI/pythia-14m on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3630

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
gradient_accumulation_steps: 4
total_train_batch_size: 4
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: 500

Training results

Training Loss	Epoch	Step	Validation Loss
40.0055	0.0005	1	9.8570
30.1781	0.0049	10	6.9379
12.6173	0.0099	20	2.6532
0.7716	0.0148	30	1.0332
4.4465	0.0198	40	0.9309
1.9365	0.0247	50	0.6442
1.6955	0.0296	60	0.6789
2.1402	0.0346	70	0.4198
1.961	0.0395	80	0.3730
2.0024	0.0444	90	0.4597
0.1765	0.0494	100	0.7099
0.996	0.0543	110	0.3618
2.5274	0.0593	120	0.3091
0.6428	0.0642	130	0.2816
0.6649	0.0691	140	0.3415
0.2853	0.0741	150	0.3710
0.6265	0.0790	160	0.3630

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.3.1+cu121
Datasets 3.0.1
Tokenizers 0.20.1