--- base_model: lordspline/mergestein tags: - axolotl - generated_from_trainer model-index: - name: mergestein results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml base_model: lordspline/mergestein model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: false strict: false chat_template: chatml datasets: # - path: lordspline/scidata # type: sharegpt # conversation: chatml - path: lordspline/wizard_v2_196k_unfiltered type: sharegpt conversation: chatml - path: lordspline/ultrainteract type: sharegpt conversation: chatml dataset_prepared_path: last_run_prepared val_set_size: 0.002 output_dir: ./mergestein sequence_len: 8192 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false wandb_project: mergestein wandb_entity: wandb_watch: wandb_name: wandb_log_model: hub_model_id: lordspline/mergestein gradient_accumulation_steps: 1 micro_batch_size: 1 num_epochs: 1 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 0.0001 # look train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: unsloth gradient_checkpointing_kwargs: use_reentrant: true # look early_stopping_patience: resume_from_checkpoint: # ./mergestein/checkpoint-8015 local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 1 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 100 debug: # deepspeed: deepspeed_configs/zero3_bf16.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: eos_token: "<|im_end|>" pad_token: "<|end_of_text|>" tokens: - "<|im_start|>" - "<|im_end|>" ```

# mergestein This model is a fine-tuned version of [lordspline/mergestein](https://huggingface.co/lordspline/mergestein) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0348 ## 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: 0.0001 - 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 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.1202 | 1.0 | 25552 | 1.0348 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.0+cu118 - Datasets 2.19.1 - Tokenizers 0.19.1