axolotl version: 0.4.0
adapter: qlora
base_model: qlora-out2/merged
bf16: false
dataset_prepared_path: null
datasets:
- data_files:
- Chain-of-Thought/formatted_cot_data/gsm8k_train.json
path: QingyiSi/Alpaca-CoT
type: alpaca:chat
debug: null
deepspeed: null
early_stopping_patience: null
evals_per_epoch: null
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: false
is_llama_derived_model: true
learning_rate: 0.0003
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
max_steps: 369
micro_batch_size: 1
mlflow_experiment_name: colab-example
model_type: LlamaForCausalLM
num_epochs: 6
optimizer: paged_adamw_32bit
output_dir: ./qlora-out
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: null
sequence_len: 2048
special_tokens:
bos_token: <s>
eos_token: </s>
unk_token: <unk>
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: true
val_set_size: 0.2
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 10
weight_decay: 0.1
xformers_attention: false
qlora-out
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7597
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: 0.0003
- 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: 10
- training_steps: 369
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7414 | 0.59 | 369 | 0.7597 |
Framework versions
- PEFT 0.8.2
- Transformers 4.39.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.0