--- license: other base_model: Qwen/Qwen1.5-7B tags: - generated_from_trainer model-index: - name: qwen1.5-7b-fft results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml base_model: Qwen/Qwen1.5-7B model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: /data/data/final_set_cleaned/train/ type: sharegpt conversation: chatml - path: /data/data/map_coig_cqia.jsonl type: sharegpt conversation: chatml - path: /data/data/ruozhiba.jsonl type: sharegpt conversation: chatml - path: /data/data/sharegpt4.jsonl type: sharegpt conversation: chatml - path: /data/data/OpenHermes-Zh.jsonl type: sharegpt conversation: chatml dataset_prepared_path: last_run_prepared val_set_size: 0 output_dir: ./out sequence_len: 8192 sample_packing: true pad_to_sequence_len: true wandb_project: FFT wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 2 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 2e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: logging_steps: 1 xformers_attention: flash_attention: true warmup_ratio: 0.05 evals_per_epoch: 0 eval_table_size: saves_per_epoch: 4 save_total_limit: 8 debug: deepspeed: deepspeed/zero2.json weight_decay: 0.0 fsdp: fsdp_config: default_system_message: "You are a helpful assistant." special_tokens: eos_token: "<|im_end|>" pad_token: "<|end_of_text|>" ```

# qwen1.5-7b-fft This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) 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-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - gradient_accumulation_steps: 8 - total_train_batch_size: 48 - total_eval_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 48 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.0.1+cu118 - Datasets 2.15.0 - Tokenizers 0.19.1