--- library_name: transformers license: cc-by-nc-4.0 base_model: hardlyworking/4Brp tags: - axolotl - generated_from_trainer datasets: - PocketDoc/Dans-Prosemaxx-RepRemover-1 model-index: - name: 4Brepremover results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.11.0.dev0` ```yaml base_model: hardlyworking/4Brp load_in_8bit: false load_in_4bit: false strict: false datasets: - path: PocketDoc/Dans-Prosemaxx-RepRemover-1 type: dan-chat-advanced val_set_size: 0 output_dir: ./outputs/out dataset_prepared_path: last_run_prepared shuffle_merged_datasets: true hub_model_id: hardlyworking/4Brepremover hub_strategy: "all_checkpoints" push_dataset_to_hub: hf_use_auth_token: true plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true liger_fused_linear_cross_entropy: false cut_cross_entropy: true sequence_len: 32768 sample_packing: true eval_sample_packing: true pad_to_sequence_len: true wandb_project: new4B wandb_entity: wandb_watch: wandb_name: new4Brep wandb_log_model: evals_per_epoch: eval_table_size: eval_max_new_tokens: gradient_accumulation_steps: 1 micro_batch_size: 8 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: offload gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true s2_attention: deepspeed: warmup_ratio: 0.05 saves_per_epoch: 1 debug: weight_decay: 0.01 fsdp: fsdp_config: special_tokens: pad_token: <|endoftext|> ```

# 4Brepremover This model is a fine-tuned version of [hardlyworking/4Brp](https://huggingface.co/hardlyworking/4Brp) on the PocketDoc/Dans-Prosemaxx-RepRemover-1 dataset. ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 8 - training_steps: 174 ### Training results ### Framework versions - Transformers 4.53.1 - Pytorch 2.6.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.2