See axolotl config
axolotl version: 0.4.1
# See:
# - https://github.com/karpathy/nanoGPT/blob/master/config/train_gpt2.py#L1
# - https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/examples/tiny-llama/pretrain.yml#L14
# - https://github.com/karpathy/nanoGPT/blob/master/train.py#L35
base_model: diwank/cryptgpt-large
hub_model_id: diwank/cryptgpt-large
model_type: GPT2LMHeadModel
tokenizer_type: AutoTokenizer
trust_remote_code: true # required for CryptGPTTokenizer
resize_token_embeddings_to_32x: true
output_dir: ./outputs/model-out
datasets:
- path: diwank/encrypted-openwebtext
type: completion
dataset_prepared_path: ./cryptgpt-prepared-dataset
val_set_size: 0.04
shuffle_merged_datasets: false
sequence_len: 1024
pad_to_sequence_len: true
sample_packing: false
pretrain_multipack_attn: false
train_on_inputs: true
gradient_accumulation_steps: 1
micro_batch_size: 128
optimizer: adamw_bnb_8bit
adam_beta1: 0.9
adam_beta2: 0.95
seed: 42
lr_scheduler: cosine
learning_rate: 6e-4
cosine_min_lr_ratio: 0.1 # min: 6e-5
weight_decay: 0.15
bf16: auto
tf32: true
flash_attention: true
torch_compile: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
deepspeed: deepspeed_configs/zero2.json
epochs: 20 # overriden by max_steps
max_steps: 600000
eval_steps: 12000
save_steps: 12000
save_total_limit: 3
early_stopping_patience: 3
auto_resume_from_checkpoints: true
logging_steps: 1
eval_max_new_tokens: 128
eval_causal_lm_metrics:
- sacrebleu
wandb_project: cryptgpt-large-0.1
wandb_name: cryptgpt-large-run-04
cryptgpt-large
This model is a fine-tuned version of diwank/cryptgpt-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8034
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: 0.0006
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 1024
- total_eval_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 20456
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
15.7656 | 0.0000 | 1 | 15.4910 |
1.8545 | 0.5866 | 12000 | 1.8034 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
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