base_model: NousResearch/Llama-2-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: caffeinatedcherrychic/cidds-agg-balanced
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 2048
sample_packing: false
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 5
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

max_steps: 500
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 1
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.001
fsdp:
fsdp_config:
special_tokens:

qlora-out

This model is a fine-tuned version of NousResearch/Llama-2-7b-hf on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1998

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: 10
training_steps: 62

Training results

Training Loss	Epoch	Step	Validation Loss
6.6299	0.08	1	6.9320
5.9686	0.32	4	4.4463
0.5956	0.64	8	0.5577
0.4848	0.96	12	0.8370
0.4913	1.28	16	0.4896
0.671	1.6	20	0.5175
2.6136	1.92	24	2.3446
0.6383	2.24	28	0.5194
0.5776	2.56	32	0.5653
0.4913	2.88	36	0.4791
0.3486	3.2	40	0.4041
0.4944	3.52	44	0.3174
0.4788	3.84	48	0.3952
0.3321	4.16	52	0.2342
0.207	4.48	56	0.2058
0.4502	4.8	60	0.1998

Framework versions

PEFT 0.10.1.dev0
Transformers 4.39.0.dev0
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.0

daveydhruti/Llama2-based-NIDS

qlora-out

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

daveydhruti/
Llama2-based-NIDS