# use google/gemma-7b if you have access
base_model: google/gemma-2b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
#lora_model_dir: 
load_in_8bit: false
load_in_4bit: true
strict: false

# huggingface repo
datasets:
  - path: ./python-oasst/cleaned-dataset.jsonl
    type: oasst
val_set_size: 0.20
output_dir: ./out1v

adapter: lora
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
gptq: false
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true

wandb_project: gemma-2b-it
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:


gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: true
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

out1v

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

Loss: 1.1243

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
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1045
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.9865	0.0	1	2.2808
1.1033	0.25	2613	1.1338
1.0035	0.5	5226	1.1308
1.0089	0.75	7839	1.1272
0.9816	1.0	10452	1.1238
0.8727	1.25	13065	1.1270
0.9951	1.5	15678	1.1254
1.01	1.75	18291	1.1242
1.0677	2.0	20904	1.1243

Framework versions

PEFT 0.10.0
Transformers 4.40.0.dev0
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.15.0

dvdmrs09/peft-gemma-2b-vXI-merged

out1v

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dvdmrs09/
peft-gemma-2b-vXI-merged