Instructions to use masatochi/tuning-0e7533c8-cd9a-4e62-88b6-79aaf3bbe621 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use masatochi/tuning-0e7533c8-cd9a-4e62-88b6-79aaf3bbe621 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "masatochi/tuning-0e7533c8-cd9a-4e62-88b6-79aaf3bbe621") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: llama3.2 | |
| base_model: unsloth/Llama-3.2-1B-Instruct | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| model-index: | |
| - name: tuning-0e7533c8-cd9a-4e62-88b6-79aaf3bbe621 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) | |
| <details><summary>See axolotl config</summary> | |
| axolotl version: `0.4.1` | |
| ```yaml | |
| adapter: lora | |
| base_model: unsloth/Llama-3.2-1B-Instruct | |
| bf16: auto | |
| chat_template: llama3 | |
| dataset_prepared_path: null | |
| datasets: | |
| - data_files: | |
| - dialogsum_train_data.json | |
| ds_type: json | |
| path: /workspace/input_data/dialogsum_train_data.json | |
| type: | |
| field_input: dialogue | |
| field_instruction: topic | |
| field_output: summary | |
| system_format: '{system}' | |
| system_prompt: '' | |
| debug: null | |
| deepspeed: null | |
| early_stopping_patience: null | |
| eval_max_new_tokens: 128 | |
| eval_table_size: null | |
| evals_per_epoch: 2 | |
| flash_attention: true | |
| fp16: null | |
| fsdp: null | |
| fsdp_config: null | |
| gradient_accumulation_steps: 8 | |
| gradient_checkpointing: true | |
| group_by_length: false | |
| hub_model_id: masatochi/tuning-0e7533c8-cd9a-4e62-88b6-79aaf3bbe621 | |
| hub_strategy: checkpoint | |
| hub_token: null | |
| learning_rate: 0.0002 | |
| load_in_4bit: false | |
| load_in_8bit: true | |
| local_rank: null | |
| logging_steps: 1 | |
| lora_alpha: 16 | |
| lora_dropout: 0.06 | |
| lora_fan_in_fan_out: null | |
| lora_model_dir: null | |
| lora_r: 8 | |
| lora_target_linear: true | |
| lr_scheduler: cosine | |
| max_steps: 200 | |
| micro_batch_size: 3 | |
| mlflow_experiment_name: /tmp/dialogsum_train_data.json | |
| model_type: LlamaForCausalLM | |
| num_epochs: 3 | |
| optimizer: adamw_bnb_8bit | |
| output_dir: miner_id_24 | |
| pad_to_sequence_len: true | |
| resume_from_checkpoint: null | |
| s2_attention: null | |
| sample_packing: false | |
| save_steps: 5 | |
| save_strategy: steps | |
| sequence_len: 4096 | |
| strict: false | |
| tf32: false | |
| tokenizer_type: AutoTokenizer | |
| train_on_inputs: false | |
| val_set_size: 0.05 | |
| wandb_entity: lkotbimehdi | |
| wandb_mode: online | |
| wandb_project: lko | |
| wandb_run: miner_id_24 | |
| wandb_runid: 0e7533c8-cd9a-4e62-88b6-79aaf3bbe621 | |
| warmup_steps: 30 | |
| weight_decay: 0.0 | |
| xformers_attention: null | |
| ``` | |
| </details><br> | |
| # tuning-0e7533c8-cd9a-4e62-88b6-79aaf3bbe621 | |
| This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0166 | |
| ## 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: 3 | |
| - eval_batch_size: 3 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 24 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 30 | |
| - training_steps: 200 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 2.5495 | 0.0018 | 1 | 2.3448 | | |
| | 1.1668 | 0.0598 | 34 | 1.1669 | | |
| | 0.9638 | 0.1196 | 68 | 1.0743 | | |
| | 1.0765 | 0.1794 | 102 | 1.0453 | | |
| | 1.1714 | 0.2392 | 136 | 1.0312 | | |
| | 0.9518 | 0.2990 | 170 | 1.0166 | | |
| ### Framework versions | |
| - PEFT 0.13.2 | |
| - Transformers 4.45.2 | |
| - Pytorch 2.4.1+cu124 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.20.1 |