adapter: lora
base_model: mhenrichsen/gemma-7b
bf16: auto
datasets:
- path: data.jsonl
  type: alpaca
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_sample_packing: false
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: FatCat87/test-task-2025-01-06-16-53-36
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 2
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: ./outputs/out
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: 1
sequence_len: 4096
special_tokens: null
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0.1
wandb_entity: fatcat87-taopanda
wandb_log_model: null
wandb_mode: online
wandb_name: test-task-2025-01-06-16-53-36
wandb_project: subnet56
wandb_runid: test-task-2025-01-06-16-53-36
wandb_watch: null
warmup_ratio: 0.1
weight_decay: 0.0
xformers_attention: null

Visualize in Weights & Biases

test-task-2025-01-06-16-53-36

This model is a fine-tuned version of mhenrichsen/gemma-7b on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0005

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 2
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.9785	0.1	1	1.1005
1.0282	0.3	3	1.0752
1.0195	0.6	6	1.0116
1.0354	0.9	9	1.0007
0.9228	1.15	12	0.9984
0.8895	1.45	15	1.0030
0.9105	1.75	18	1.0005

Framework versions

PEFT 0.11.1
Transformers 4.42.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1