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isafpr-gemma-qlora-templatefree

PEFT
Safetensors
gemma
axolotl
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metadata
license: gemma
library_name: peft
tags:
  - axolotl
  - generated_from_trainer
base_model: google/gemma-2b
model-index:
  - name: isafpr-gemma-qlora-templatefree
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.1

# use google/gemma-7b if you have access
base_model: google/gemma-2b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: data/templatefree_isaf_press_releases_ft_train.jsonl
    type: input_output
val_set_size: 0.1
output_dir: ./outputs/gemma/qlora-out-templatefree
hub_model_id: strickvl/isafpr-gemma-qlora-templatefree

adapter: qlora
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_modules_to_save:
 - embed_tokens
 - lm_head

sequence_len: 1024
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: isaf_pr_ft
wandb_entity: strickvl
wandb_watch:
wandb_name:
wandb_log_model:


gradient_accumulation_steps: 3
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
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:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"

isafpr-gemma-qlora-templatefree

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: 0.0379

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
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 3
  • total_train_batch_size: 12
  • total_eval_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 64
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss
2.3995 0.0054 1 2.3804
0.1051 0.2527 47 0.0906
0.0444 0.5054 94 0.0617
0.0292 0.7581 141 0.0490
0.1049 1.0108 188 0.0475
0.03 1.2419 235 0.0435
0.0219 1.4946 282 0.0411
0.0286 1.7473 329 0.0413
0.0403 2.0 376 0.0383
0.0274 2.2330 423 0.0386
0.0178 2.4857 470 0.0384
0.0272 2.7384 517 0.0378
0.0409 2.9910 564 0.0371
0.013 3.2240 611 0.0378
0.0177 3.4767 658 0.0380
0.018 3.7294 705 0.0379

Framework versions

  • PEFT 0.11.1
  • Transformers 4.41.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1