See axolotl config

axolotl version: 0.4.0

adapter: null
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
bf16: auto
dataset_prepared_path: last_run_prepared
datasets:
- path: utrgvseniorproject/medtext
  type: completion
debug: null
deepspeed: null
early_stopping_patience: null
eval_sample_packing: false
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_fuse_mlp: true
flash_attn_fuse_qkv: false
flash_attn_rms_norm: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: false
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: null
lora_dropout: null
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: null
lora_target_linear: null
lr_scheduler: cosine
micro_batch_size: 1
model_type: LlamaForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: ./TinyLlama-preprocess-medtext-epochs-1-lr-0002
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: 1
sequence_len: 2048
special_tokens: null
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: utrgvmedai
wandb_log_model: null
wandb_name: tinyLama_colab_test_4
wandb_project: TinyLlama-preprocess-medtext-epochs-1-lr-0002
wandb_watch: null
warmup_steps: 100
weight_decay: 0.1
xformers_attention: null

TinyLlama-preprocess-medtext-epochs-1-lr-0002

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: 2.6325

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: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.7582	0.0	1	2.1282
2.6905	0.25	155	4.0796
2.9887	0.5	310	2.8330
2.6398	0.75	465	2.7038
1.7458	1.0	620	2.6325

Framework versions

• Transformers 4.38.2
• Pytorch 2.1.2+cu121
• Datasets 2.18.0
• Tokenizers 0.15.0