--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen3.5-27B tags: - axolotl - base_model:adapter:Qwen/Qwen3.5-27B - lora - transformers datasets: - output.parquet pipeline_tag: text-generation model-index: - name: model-output results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.16.0.dev0` ```yaml # === Model Configuration === base_model: Qwen/Qwen3.5-27B load_in_8bit: false load_in_4bit: false # === Training Setup === num_epochs: 2 micro_batch_size: 8 gradient_accumulation_steps: 4 sequence_len: 8192 sample_packing: true pad_to_sequence_len: true adapter: lora lora_r: 64 lora_alpha: 512 lora_target_modules: - q_proj - k_proj - v_proj - o_proj - down_proj - up_proj - linear_attn.in_proj_qkv - linear_attn.in_proj_z - linear_attn.out_proj # === Hyperparameter Configuration === optimizer: adamw_torch_8bit learning_rate: 1e-5 lr_scheduler: constant weight_decay: 0.001 max_grad_norm: 0.1 warmup_ratio: 0.05 cosine_min_lr_ratio: 0.1 # === Data Configuration === datasets: - path: output.parquet ds_type: parquet type: chat_template: tokenizer_default dataset_prepared_path: last_run_prepared # === Hardware Optimization === gradient_checkpointing: offload # === Wandb Tracking === wandb_project: qwen-27b-seemo # === 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 plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin ```

# model-output This model is a fine-tuned version of [Qwen/Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B) on the output.parquet 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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: 4 - training_steps: 90 ### Training results ### Framework versions - PEFT 0.18.1 - Transformers 5.3.0 - Pytorch 2.8.0+cu128 - Datasets 4.5.0 - Tokenizers 0.22.2