base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: true
load_in_4bit: false
strict: false

data_seed: 2606
seed: 2606

datasets:
  - path: data/templatefree_isaf_press_releases_ft_train.jsonl
    type: input_output
dataset_prepared_path:
val_set_size: 0.1
output_dir: tiny-llama/lora-out
hub_model_id: Peaky8linders/isafpr-tiny-llama-lora

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
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_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

isafpr-tiny-llama-lora

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0395

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: 2606
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: 10
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
1.7938	0.0138	1	1.7961
0.2755	0.2483	18	0.2099
0.0937	0.4966	36	0.0798
0.0625	0.7448	54	0.0646
0.0507	0.9931	72	0.0581
0.0466	1.2138	90	0.0516
0.0391	1.4621	108	0.0485
0.0534	1.7103	126	0.0457
0.0611	1.9586	144	0.0439
0.0281	2.1793	162	0.0434
0.0382	2.4276	180	0.0416
0.031	2.6759	198	0.0407
0.0278	2.9241	216	0.0400
0.0377	3.1448	234	0.0397
0.0247	3.3931	252	0.0400
0.0419	3.6414	270	0.0395
0.0273	3.8897	288	0.0395

Framework versions

PEFT 0.11.1
Transformers 4.41.1
Pytorch 2.1.2+cu118
Datasets 2.19.1
Tokenizers 0.19.1