--- library_name: transformers license: llama3 base_model: arcee-ai/Llama-3.1-SuperNova-Lite tags: - generated_from_trainer model-index: - name: outputs results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml base_model: arcee-ai/Llama-3.1-SuperNova-Lite model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: NewEden/CharacterAI-logs-sharegpt-Ngram-Cleaned type: sharegpt conversation: llama3 - path: NewEden/OpenCAI-ShareGPT type: sharegpt conversation: llama3 chat_template: llama3 #val_set_size: 0.01 output_dir: ./outputs adapter: lora_r: lora_alpha: lora_dropout: lora_target_linear: sequence_len: 16384 # sequence_len: 32768 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true wandb_project: CAI-Supernova wandb_entity: wandb_watch: wandb_name: CAI-Supernova-1 wandb_log_model: plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true gradient_accumulation_steps: 4 micro_batch_size: 1 num_epochs: 4 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 2e-6 weight_decay: 0.05 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: unsloth early_stopping_patience: resume_from_checkpoint: #auto_resume_from_checkpoints: true local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 5 #evals_per_epoch: 4 eval_table_size: #eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16_cpuoffload_params.json fsdp: fsdp_config: special_tokens: pad_token: <|finetune_right_pad_id|> eos_token: <|eot_id|> ```

# outputs This model is a fine-tuned version of [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite) on the None 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: 2e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1