--- library_name: transformers tags: - generated_from_trainer datasets: - WokeAI/tankie-seed20-gens model-index: - name: model-output results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.15.0.dev0` ```yaml # === Model Configuration === base_model: /root/Nanbeige4-3B-Thinking-2511 trust_remote_code: true load_in_8bit: false load_in_4bit: false # === Training Setup === num_epochs: 2 micro_batch_size: 2 gradient_accumulation_steps: 1 sequence_len: 8192 sample_packing: true pad_to_sequence_len: true # === Hyperparameter Configuration === optimizer: adamw_torch_8bit learning_rate: 5e-5 lr_scheduler: constant weight_decay: 0.001 max_grad_norm: 0.1 warmup_ratio: 0.2 cosine_min_lr_ratio: 0.1 # === Data Configuration === datasets: - path: WokeAI/tankie-seed20-gens type: chat_template split: train chat_template: tokenizer_default dataset_prepared_path: last_run_prepared # === Hardware Optimization === gradient_checkpointing: offload # === Wandb Tracking === wandb_project: polititune-3-wip-warmup # === Checkpointing === saves_per_epoch: 1 # === Advanced Settings === output_dir: ./model-output bf16: auto flash_attention: true train_on_inputs: false group_by_length: false logging_steps: 1 trust_remote_code: false fsdp: - auto_wrap - full_shard fsdp_config: fsdp_version: 2 fsdp_offload_params: false fsdp_cpu_ram_efficient_loading: true fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer fsdp_state_dict_type: SHARDED_STATE_DICT fsdp_sharding_strategy: FULL_SHARD fsdp_reshard_after_forward: true fsdp_activation_checkpointing: true # will disable if doesnt work ```

# model-output This model was trained from scratch on the WokeAI/tankie-seed20-gens 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-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 8 - training_steps: 44 ### Training results ### Framework versions - Transformers 5.0.0 - Pytorch 2.8.0+cu128 - Datasets 4.5.0 - Tokenizers 0.22.2