base_model: jeiku/Chikn_4B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: FourOhFour/RP_Phase
    type: sharegpt
    conversation: chatml

chat_template: chatml

val_set_size: 0.01
output_dir: ./outputs/out

adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

wandb_project: final4B
wandb_entity:
wandb_watch:
wandb_name: final4B
wandb_log_model:

gradient_accumulation_steps: 12
micro_batch_size: 3
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000008
weight_decay: 0.05

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

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: 2

debug:
deepspeed: deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:

special_tokens:
  pad_token: <|finetune_right_pad_id|>

outputs/out

This model is a fine-tuned version of jeiku/Chikn_4B on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.6856

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: 8e-06
train_batch_size: 3
eval_batch_size: 3
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 12
total_train_batch_size: 72
total_eval_batch_size: 6
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 22
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.8733	0.0089	1	3.2193
1.8216	0.25	28	2.8722
1.785	0.5	56	2.7828
1.7331	0.75	84	2.7508
1.7098	1.0	112	2.7077
1.6084	1.2465	140	2.7037
1.6426	1.4966	168	2.6923
1.6159	1.7468	196	2.6870
1.6379	1.9970	224	2.6856

Framework versions

Transformers 4.46.0.dev0
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.20.0

FourOhFour/Nuggies_4B

outputs/out

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

FourOhFour/
Nuggies_4B