See axolotl config

axolotl version: 0.4.1

base_model: unsloth/Meta-Llama-3.1-8B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: chatml
datasets:
  - path: Howard881010/medical-4day
    type: alpaca
    train_on_split: train
dataset_prepared_path:
output_dir: ./finetune/output/medical-4day

test_datasets:
  - path: Howard881010/medical-4day
    split: valid
    type: alpaca

adapter: lora
lora_model_dir:

sequence_len: 3720
sample_packing: false
pad_to_sequence_len: true

lora_r: 8
lora_alpha: 32
lora_dropout: 0.1
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: finetune
wandb_entity:
wandb_watch:
wandb_name: medical-4day
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_hf
learning_rate: 0.00002
max_grad_norm: 1.0

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

gradient_checkpointing: true
logging_steps: 1
xformers_attention: 
flash_attention: true
eval_sample_packing: False

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
seed: 0
special_tokens:
  pad_token: "<|end_of_text|>"

finetune/output/medical-4day

This model is a fine-tuned version of unsloth/Meta-Llama-3.1-8B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.8058

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: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 6
• total_train_batch_size: 24
• total_eval_batch_size: 24
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.3464	0.0011	1	1.1381
1.0071	0.2508	238	0.8342
0.9181	0.5016	476	0.8135
0.9444	0.7524	714	0.8058

Framework versions

• PEFT 0.12.0
• Transformers 4.44.0
• Pytorch 2.4.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1