base_model: Undi95/Meta-Llama-3-8B-hf
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: Pbug/bftest
    type: sharegpt
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 10
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
eval_sample_packing: False
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

lora-out

This model is a fine-tuned version of Undi95/Meta-Llama-3-8B-hf on the None dataset. It achieves the following results on the evaluation set:

Loss: nan

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: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
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: 100
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss
2.5419	0.03	1	nan
2.5492	0.51	18	nan
2.434	1.01	36	nan
2.3504	1.5	54	nan
2.3643	2.0	72	nan
2.2834	2.48	90	nan
2.2383	2.98	108	nan
1.8786	3.47	126	nan
1.7963	3.98	144	nan
2.1853	4.47	162	nan
1.4333	4.98	180	nan
1.2058	5.46	198	nan
1.125	5.96	216	nan
0.809	6.44	234	nan
0.7118	6.94	252	nan
0.7175	7.44	270	nan
0.7341	7.94	288	nan
0.774	8.44	306	nan
0.6379	8.94	324	nan
0.562	9.41	342	nan

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Pbug/bf_rp_base

lora-out

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Pbug/
bf_rp_base