--- base_model: winglian/m12b-20240721-test010 tags: - generated_from_trainer model-index: - name: outputs/simpo-out results: [] license: apache-2.0 --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml base_model: winglian/m12b-20240721-test010 tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: false strict: false chat_template: chatml rl: simpo rl_beta: 2.5 cpo_alpha: 0.05 simpo_gamma: 0.1 datasets: - path: princeton-nlp/gemma2-ultrafeedback-armorm type: chat_template.default chat_template: chatml field_messages: chosen field_chosen: chosen field_rejected: rejected message_field_role: role message_field_content: content roles: system: - system user: - user assistant: - assistant dataset_prepared_path: val_set_size: 0.0 output_dir: ./outputs/simpo-out save_safetensors: true save_only_model: true # fsdp seems to crap out saving the optimizer sequence_len: 8192 sample_packing: false pad_to_sequence_len: true adapter: lora_model_dir: lora_r: 256 lora_alpha: 256 lora_dropout: 0.1 lora_target_linear: true lora_fan_in_fan_out: # peft_use_rslora: true wandb_project: romulus-12b wandb_entity: oaaic wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 16 micro_batch_size: 1 num_epochs: 1 optimizer: adamw_torch lr_scheduler: cosine learning_rate: 5.0e-7 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true s2_attention: warmup_steps: 25 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json weight_decay: 0.0 fsdp: fsdp_config: ```

[Visualize in Weights & Biases](https://wandb.ai/oaaic/romulus-12b/runs/y53osmua) # outputs/simpo-out This model is a fine-tuned version of [winglian/m12b-20240721-test010](https://huggingface.co/winglian/m12b-20240721-test010) on an unknown 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: 5e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 25 - training_steps: 466 ### Training results ### Framework versions - Transformers 4.43.1 - Pytorch 2.3.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1