--- license: apache-2.0 library_name: peft tags: - axolotl - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T model-index: - name: isafpr-tiny-llama-lora-sharegpt results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false # I'm training on 4090 GPUs # so I'm using 4-bit precision to save on memory load_in_4bit: true strict: false data_seed: 42 seed: 42 datasets: - path: data/sharegpt_isaf_press_releases_ft_train.jsonl type: sharegpt conversation: alpaca dataset_prepared_path: val_set_size: 0.1 output_dir: ./outputs/tiny-llama/lora-out-sharegpt hub_model_id: strickvl/isafpr-tiny-llama-lora-sharegpt sequence_len: 4096 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true 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: isaf_pr_ft wandb_entity: strickvl wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 4 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "" eos_token: "" ```

# isafpr-tiny-llama-lora-sharegpt This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0507 ## 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: 0.0002 - 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.7687 | 0.0270 | 1 | 1.7719 | | 1.0632 | 0.2703 | 10 | 0.9033 | | 0.1374 | 0.5405 | 20 | 0.1365 | | 0.0763 | 0.8108 | 30 | 0.0942 | | 0.0752 | 1.0608 | 40 | 0.0765 | | 0.0764 | 1.3311 | 50 | 0.0680 | | 0.0623 | 1.6014 | 60 | 0.0630 | | 0.0596 | 1.8716 | 70 | 0.0593 | | 0.0523 | 2.1216 | 80 | 0.0570 | | 0.0514 | 2.3919 | 90 | 0.0543 | | 0.0501 | 2.6622 | 100 | 0.0528 | | 0.0475 | 2.9324 | 110 | 0.0515 | | 0.0525 | 3.1824 | 120 | 0.0511 | | 0.0436 | 3.4527 | 130 | 0.0509 | | 0.0508 | 3.7230 | 140 | 0.0507 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1