--- license: other library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen1.5-7B model-index: - name: home/yujia/home/CN_Hateful/trained_models/qwen/toxi/1e-5/ results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml # base_model: Qwen/Qwen-7B base_model: Qwen/Qwen1.5-7B model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer trust_remote_code: true load_in_8bit: true load_in_4bit: false strict: false datasets: # - path: mhenrichsen/alpaca_2k_test # - path: /home/yujia/home/CN_Hateful/train_toxiCN_cn.json - path: /home/yujia/home/CN_Hateful/train_toxiCN.json ds_type: json type: alpaca dataset_prepared_path: val_set_size: 0.05 # output_dir: /home/yujia/home/CN_Hateful/trained_models/qwen/CN/toxi/3e-5/ output_dir: /home/yujia/home/CN_Hateful/trained_models/qwen/toxi/1e-5/ sequence_len: 256 # supports up to 8192 sample_packing: false pad_to_sequence_len: adapter: lora lora_model_dir: lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.00001 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: false early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 20 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ```

# home/yujia/home/CN_Hateful/trained_models/qwen/toxi/1e-5/ This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0540 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - 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: 10 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.4697 | 0.0 | 1 | 3.5475 | | 0.0881 | 0.25 | 142 | 0.0819 | | 0.1131 | 0.5 | 284 | 0.0763 | | 0.0538 | 0.75 | 426 | 0.0732 | | 0.0425 | 1.0 | 568 | 0.0656 | | 0.0866 | 1.26 | 710 | 0.0582 | | 0.0705 | 1.51 | 852 | 0.0593 | | 0.0848 | 1.76 | 994 | 0.0562 | | 0.0631 | 2.01 | 1136 | 0.0552 | | 0.0299 | 2.26 | 1278 | 0.0551 | | 0.0494 | 2.51 | 1420 | 0.0545 | | 0.0417 | 2.76 | 1562 | 0.0540 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0