--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-3B tags: - generated_from_trainer model-index: - name: outputs/lora-out results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml base_model: meta-llama/Llama-3.2-3B model_type: LlamaForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: json data_files: "data/amendments_with_content_converted.json" type: completion - path: json data_files: "data/federal_rules_converted.json" type: completion - path: json data_files: "data/cornell_legal_encyclopedias_converted.json" type: completion - path: json data_files: "data/pocket_guide_for_judges_converted.json" type: completion - path: json data_files: "data/us_federal_code.json" type: completion - path: json data_files: "data/us_supreme_court_summaries_converted.json" type: completion - path: json data_files: "data/us_supreme_court_converted.json" type: completion - path: json data_files: "data/ucfr.json" type: completion - path: json data_files: "data/map-code-filtered.json" type: completion dataset_prepared_path: val_set_size: 0.05 output_dir: ./outputs/lora-out sequence_len: 8192 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true # adapter: lora # lora_model_dir: # lora_r: 128 # lora_alpha: 32 # lora_dropout: 0.05 # lora_target_linear: true # lora_fan_in_fan_out: # lora_modules_to_save: # - embed_tokens # - lm_head unfrozen_parameters: - ^lm_head.weight$ - ^model.embed_tokens.weight$ # mlp.down_proj layers - model.layers.0.mlp.down_proj - model.layers.1.mlp.down_proj - model.layers.17.mlp.down_proj - model.layers.19.mlp.down_proj - model.layers.18.mlp.down_proj - model.layers.5.mlp.down_proj - model.layers.20.mlp.down_proj - model.layers.2.mlp.down_proj - model.layers.4.mlp.down_proj - model.layers.6.mlp.down_proj - model.layers.3.mlp.down_proj - model.layers.16.mlp.down_proj - model.layers.15.mlp.down_proj - model.layers.13.mlp.down_proj # mlp.gate_proj layers - model.layers.0.mlp.gate_proj - model.layers.1.mlp.gate_proj - model.layers.2.mlp.gate_proj - model.layers.3.mlp.gate_proj - model.layers.22.mlp.gate_proj - model.layers.21.mlp.gate_proj - model.layers.20.mlp.gate_proj - model.layers.23.mlp.gate_proj - model.layers.19.mlp.gate_proj - model.layers.4.mlp.gate_proj - model.layers.18.mlp.gate_proj - model.layers.17.mlp.gate_proj - model.layers.5.mlp.gate_proj - model.layers.24.mlp.gate_proj # mlp.up_proj layers - model.layers.4.mlp.up_proj - model.layers.3.mlp.up_proj - model.layers.5.mlp.up_proj - model.layers.6.mlp.up_proj - model.layers.7.mlp.up_proj - model.layers.2.mlp.up_proj - model.layers.8.mlp.up_proj - model.layers.14.mlp.up_proj - model.layers.13.mlp.up_proj - model.layers.11.mlp.up_proj - model.layers.9.mlp.up_proj - model.layers.1.mlp.up_proj - model.layers.15.mlp.up_proj - model.layers.12.mlp.up_proj # self_attn.k_proj layers - model.layers.25.self_attn.k_proj - model.layers.22.self_attn.k_proj - model.layers.19.self_attn.k_proj - model.layers.20.self_attn.k_proj - model.layers.17.self_attn.k_proj - model.layers.24.self_attn.k_proj - model.layers.23.self_attn.k_proj - model.layers.18.self_attn.k_proj - model.layers.21.self_attn.k_proj - model.layers.27.self_attn.k_proj - model.layers.15.self_attn.k_proj - model.layers.10.self_attn.k_proj - model.layers.6.self_attn.k_proj - model.layers.5.self_attn.k_proj # self_attn.o_proj layers wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 3 optimizer: paged_adamw_32bit # Gradient clipping max norm max_grad_norm: 1.0 noisy_embedding_alpha: 0 # no noisy embedding to ensure maximal memorization 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 s2_attention: warmup_steps: 690 evals_per_epoch: 2 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: deepspeed_configs/zero3.json weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: <|end_of_text|> ```

# outputs/lora-out This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co./meta-llama/Llama-3.2-3B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6802 ## 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: 690 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.3589 | 0.0004 | 1 | 1.5640 | | 0.9936 | 0.4984 | 1154 | 0.9440 | | 0.8384 | 0.9968 | 2308 | 0.8392 | | 0.8226 | 1.4963 | 3462 | 0.7802 | | 0.6568 | 1.9949 | 4616 | 0.7059 | | 0.5163 | 2.4923 | 5770 | 0.6886 | | 0.492 | 2.9922 | 6924 | 0.6802 | ### Framework versions - Transformers 4.45.0 - Pytorch 2.3.1+cu121 - Datasets 2.21.0 - Tokenizers 0.20.0