--- base_model: diwank/cryptgpt tags: - axolotl - generated_from_trainer model-index: - name: cryptgpt results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml # 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 hub_model_id: diwank/cryptgpt 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: 64 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: 0.1 bf16: auto tf32: true flash_attention: true torch_compile: true gradient_checkpointing: false deepspeed: deepspeed_configs/zero2.json max_steps: 1200000 eval_steps: 12000 save_steps: 12000 auto_resume_from_checkpoints: true logging_steps: 1 eval_max_new_tokens: 128 eval_causal_lm_metrics: - sacrebleu wandb_project: cryptgpt-0.1 wandb_name: cryptgpt-run-07 ```

# cryptgpt This model is a fine-tuned version of [diwank/cryptgpt](https://huggingface.co/diwank/cryptgpt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2717 ## 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: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 512 - total_eval_batch_size: 512 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 40912 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | 10.9453 | 0.0000 | 1 | 10.9383 | | 3.0117 | 0.2933 | 12000 | 2.8623 | | 2.5234 | 0.5866 | 24000 | 2.4040 | | 2.3398 | 0.8799 | 36000 | 2.2717 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.2+cu118 - Datasets 2.19.1 - Tokenizers 0.19.1