--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-8B-Base tags: - generated_from_trainer datasets: - allura-org/inkmix-v3.0 model-index: - name: ephemeral/ckpts results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.10.0.dev0` ```yaml # === Model Configuration === base_model: Qwen/Qwen3-8B-Base load_in_8bit: false load_in_4bit: false # === Training Setup === num_epochs: 2 micro_batch_size: 32 gradient_accumulation_steps: 1 sequence_len: 8192 sample_packing: true pad_to_sequence_len: true # === Hyperparameter Configuration === optimizer: apollo_adamw_layerwise # Apollo-mini configuration: optim_args: "proj=random,rank=1,scale=128.0,scale_type=tensor,update_proj_gap=200" # Regular Apollo configuration: # optim_args: optim_target_modules: all_linear learning_rate: 2e-5 lr_scheduler: rex weight_decay: 0.01 warmup_ratio: 0 # === Data Configuration === datasets: - path: allura-org/inkmix-v3.0 type: chat_template split: train field_messages: conversations message_field_role: from message_field_content: value dataset_prepared_path: last_run_prepared chat_template: chatml # === Plugins === plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin # === Hardware Optimization === gradient_checkpointing: unsloth gradient_checkpointing_kwargs: use_reentrant: false liger_rope: true liger_rms_norm: true liger_glu_activation: true cut_cross_entropy: true # === Wandb Tracking === wandb_project: qwen3-8b-inkmix-v3 # === Checkpointing === saves_per_epoch: 2 save_total_limit: 3 # === Advanced Settings === output_dir: /ephemeral/ckpts bf16: auto flash_attention: true train_on_inputs: false group_by_length: false logging_steps: 1 trust_remote_code: true ```

# ephemeral/ckpts This model is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) on the allura-org/inkmix-v3.0 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-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use apollo_adamw_layerwise with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=proj=random,rank=1,scale=128.0,scale_type=tensor,update_proj_gap=200 - lr_scheduler_type: cosine - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1