See axolotl config

axolotl version: 0.4.0

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

datasets:
  - path: kareemamrr/databricks-dolly-3k
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out

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:

hub_model_id: kareemamrr/tinyllama-1.1B_dolly-3k_lora

wandb_project: tinyllama-1.1B_dolly-3k
wandb_entity: kamr54
wandb_name: lora

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:

tinyllama-1.1B_dolly-3k_lora

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on a subset of the Databricks Dolly dataset. It achieves the following results on the evaluation set:

• Loss: 1.7510

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
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
1.9375	0.0476	1	2.1187
1.9297	0.2857	6	2.0186
1.8257	0.5714	12	1.8111
1.7285	0.8571	18	1.7862
1.752	1.1071	24	1.7848
1.8686	1.3929	30	1.7808
1.6451	1.6786	36	1.7594
1.7861	1.9643	42	1.7582
1.6606	2.2024	48	1.7536
1.6168	2.4881	54	1.7543
1.7087	2.7738	60	1.7527
1.7276	3.0119	66	1.7525
1.8117	3.2976	72	1.7487
1.6897	3.5833	78	1.7502
1.7861	3.8690	84	1.7510

Framework versions

• PEFT 0.10.0
• Transformers 4.40.2
• Pytorch 2.1.2+cu118
• Datasets 2.19.1
• Tokenizers 0.19.1