Instructions to use cilooor/1c889fdf-2371-4402-86dc-73619ad226c6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cilooor/1c889fdf-2371-4402-86dc-73619ad226c6 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-135M") model = PeftModel.from_pretrained(base_model, "cilooor/1c889fdf-2371-4402-86dc-73619ad226c6") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: apache-2.0 | |
| base_model: unsloth/SmolLM2-135M | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| model-index: | |
| - name: 1c889fdf-2371-4402-86dc-73619ad226c6 | |
| 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/SmolLM2-135M | |
| bf16: true | |
| chat_template: llama3 | |
| data_processes: 24 | |
| dataset_prepared_path: null | |
| datasets: | |
| - data_files: | |
| - 1b765a8f09016161_train_data.json | |
| ds_type: json | |
| format: custom | |
| path: /workspace/input_data/1b765a8f09016161_train_data.json | |
| type: | |
| field_input: context | |
| field_instruction: question | |
| field_output: answer | |
| 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: 4 | |
| eval_batch_size: 4 | |
| eval_max_new_tokens: 128 | |
| eval_steps: 50 | |
| eval_table_size: null | |
| evals_per_epoch: null | |
| flash_attention: true | |
| fp16: false | |
| fsdp: null | |
| fsdp_config: null | |
| gradient_accumulation_steps: 4 | |
| gradient_checkpointing: true | |
| group_by_length: true | |
| hub_model_id: cilooor/1c889fdf-2371-4402-86dc-73619ad226c6 | |
| hub_repo: null | |
| hub_strategy: checkpoint | |
| hub_token: null | |
| learning_rate: 9.0e-05 | |
| load_in_4bit: false | |
| load_in_8bit: false | |
| local_rank: null | |
| logging_steps: 1 | |
| lora_alpha: 128 | |
| lora_dropout: 0.04 | |
| lora_fan_in_fan_out: null | |
| lora_model_dir: null | |
| lora_r: 64 | |
| lora_target_linear: true | |
| lr_scheduler: cosine | |
| lr_scheduler_warmup_steps: 50 | |
| max_grad_norm: 1.0 | |
| max_memory: | |
| 0: 75GB | |
| max_steps: 200 | |
| micro_batch_size: 8 | |
| mlflow_experiment_name: /tmp/1b765a8f09016161_train_data.json | |
| model_type: AutoModelForCausalLM | |
| num_epochs: 3 | |
| optim_args: | |
| adam_beta1: 0.9 | |
| adam_beta2: 0.95 | |
| adam_epsilon: 1e-8 | |
| 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: 50 | |
| saves_per_epoch: null | |
| seed: 17333 | |
| sequence_len: 1024 | |
| strict: false | |
| tf32: true | |
| tokenizer_type: AutoTokenizer | |
| total_train_batch_size: 32 | |
| train_batch_size: 8 | |
| train_on_inputs: false | |
| trust_remote_code: true | |
| val_set_size: 0.05 | |
| wandb_entity: null | |
| wandb_mode: online | |
| wandb_name: ea037ae8-2c1d-4160-8855-461868eb7d63 | |
| wandb_project: Gradients-On-Demand | |
| wandb_run: your_name | |
| wandb_runid: ea037ae8-2c1d-4160-8855-461868eb7d63 | |
| warmup_steps: 10 | |
| weight_decay: 0.0 | |
| xformers_attention: null | |
| ``` | |
| </details><br> | |
| # 1c889fdf-2371-4402-86dc-73619ad226c6 | |
| This model is a fine-tuned version of [unsloth/SmolLM2-135M](https://huggingface.co/unsloth/SmolLM2-135M) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.4883 | |
| ## 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: 9e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 4 | |
| - seed: 17333 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 32 | |
| - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-8 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 10 | |
| - training_steps: 200 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 2.3315 | 0.0044 | 1 | 2.2132 | | |
| | 1.6806 | 0.2179 | 50 | 1.6402 | | |
| | 1.6454 | 0.4357 | 100 | 1.5338 | | |
| | 1.5968 | 0.6536 | 150 | 1.4953 | | |
| | 1.6246 | 0.8715 | 200 | 1.4883 | | |
| ### Framework versions | |
| - PEFT 0.13.2 | |
| - Transformers 4.46.0 | |
| - Pytorch 2.5.0+cu124 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.20.1 |