--- license: apache-2.0 library_name: peft tags: - axolotl - generated_from_trainer base_model: tiiuae/falcon-7b model-index: - name: test-task-2025-01-06-19-02-10 results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml adapter: lora base_model: tiiuae/falcon-7b bf16: auto datasets: - path: data.jsonl type: alpaca debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_sample_packing: false eval_table_size: null evals_per_epoch: 4 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: FatCat87/test-task-2025-01-06-19-02-10 learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_r: 32 lora_target_linear: true lr_scheduler: cosine micro_batch_size: 2 model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: ./outputs/out pad_to_sequence_len: true resume_from_checkpoint: null sample_packing: true saves_per_epoch: 1 sequence_len: 4096 special_tokens: pad_token: <|endoftext|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false val_set_size: 0.1 wandb_entity: fatcat87-taopanda wandb_log_model: null wandb_mode: online wandb_name: test-task-2025-01-06-19-02-10 wandb_project: subnet56 wandb_runid: test-task-2025-01-06-19-02-10 wandb_watch: null warmup_ratio: 0.1 weight_decay: 0.0 xformers_attention: null ```

[Visualize in Weights & Biases](https://wandb.ai/fatcat87-taopanda/subnet56/runs/wkec14vc) # test-task-2025-01-06-19-02-10 This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1264 ## 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: 27 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.2697 | 0.0072 | 1 | 1.6099 | | 1.0992 | 0.2518 | 35 | 1.3500 | | 1.0617 | 0.5036 | 70 | 1.2067 | | 1.0583 | 0.7554 | 105 | 1.1780 | | 1.0331 | 1.0072 | 140 | 1.1443 | | 1.3871 | 1.2392 | 175 | 1.1334 | | 1.3272 | 1.4910 | 210 | 1.1283 | | 1.027 | 1.7428 | 245 | 1.1264 | ### Framework versions - PEFT 0.11.1 - Transformers 4.42.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1