--- library_name: peft license: apache-2.0 base_model: tiiuae/falcon-rw-1b tags: - axolotl - generated_from_trainer model-index: - name: ad3f1a31-103e-44cc-ba52-e84774b72899 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml adapter: lora base_model: tiiuae/falcon-rw-1b bf16: auto chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 668c18f609cf0044_train_data.json ds_type: json format: custom path: /workspace/input_data/668c18f609cf0044_train_data.json type: field_input: phonetic field_instruction: tibetan field_output: english format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 1 eval_batch_size: 8 eval_max_new_tokens: 128 eval_steps: 25 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 32 gradient_checkpointing: true group_by_length: true hub_model_id: bbytxt/ad3f1a31-103e-44cc-ba52-e84774b72899 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0003 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 70GB max_steps: 200 micro_batch_size: 1 mlflow_experiment_name: /tmp/668c18f609cf0044_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1028 special_tokens: pad_token: <|endoftext|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 50 wandb_entity: null wandb_mode: online wandb_name: ad3f1a31-103e-44cc-ba52-e84774b72899 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: ad3f1a31-103e-44cc-ba52-e84774b72899 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ```

# ad3f1a31-103e-44cc-ba52-e84774b72899 This model is a fine-tuned version of [tiiuae/falcon-rw-1b](https://huggingface.co/tiiuae/falcon-rw-1b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0015 ## 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: 0.0003 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 131.1444 | 0.0004 | 1 | 5.0328 | | 113.9001 | 0.0089 | 25 | 3.5782 | | 110.3944 | 0.0178 | 50 | 3.4264 | | 106.5802 | 0.0266 | 75 | 3.1804 | | 94.4753 | 0.0355 | 100 | 3.2205 | | 94.089 | 0.0444 | 125 | 3.0302 | | 91.5495 | 0.0533 | 150 | 3.1269 | | 96.1017 | 0.0622 | 175 | 3.0286 | | 82.6931 | 0.0710 | 200 | 3.0015 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1