Instructions to use aaditya/gemma_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aaditya/gemma_out with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-7b") model = PeftModel.from_pretrained(base_model, "aaditya/gemma_out") - Notebooks
- Google Colab
- Kaggle
| license: other | |
| library_name: peft | |
| tags: | |
| - generated_from_trainer | |
| base_model: google/gemma-7b | |
| model-index: | |
| - name: out | |
| 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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) | |
| <details><summary>See axolotl config</summary> | |
| axolotl version: `0.4.0` | |
| ```yaml | |
| base_model: google/gemma-7b | |
| model_type: AutoModelForCausalLM | |
| tokenizer_type: AutoTokenizer | |
| load_in_8bit: false | |
| load_in_4bit: true | |
| strict: false | |
| datasets: | |
| - path: combined_file.json | |
| ds_type: json | |
| type: alpaca | |
| val_set_size: 0.1 | |
| output_dir: ./out | |
| adapter: qlora | |
| lora_r: 32 | |
| lora_alpha: 16 | |
| lora_dropout: 0.05 | |
| lora_target_linear: true | |
| lora_target_modules: | |
| - q_proj | |
| - v_proj | |
| - v_proj | |
| - o_proj | |
| - gate_proj | |
| - down_proj | |
| - up_proj | |
| sequence_len: 4096 | |
| sample_packing: false | |
| pad_to_sequence_len: false | |
| wandb_project: gemma_results | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_name: | |
| wandb_log_model: | |
| gradient_accumulation_steps: 3 | |
| micro_batch_size: 2 | |
| num_epochs: 1 | |
| optimizer: adamw_bnb_8bit | |
| lr_scheduler: cosine | |
| learning_rate: 5e-5 | |
| 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 | |
| warmup_ratio: 0.1 | |
| evals_per_epoch: 1 | |
| eval_table_size: | |
| eval_max_new_tokens: 128 | |
| saves_per_epoch: 1 | |
| debug: | |
| deepspeed: | |
| weight_decay: 0.0 | |
| fsdp: | |
| fsdp_config: | |
| special_tokens: | |
| ``` | |
| </details><br> | |
| # out | |
| This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0418 | |
| ## 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: 5e-05 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - gradient_accumulation_steps: 3 | |
| - total_train_batch_size: 6 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 2092 | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:-----:|:---------------:| | |
| | 1.1309 | 1.0 | 20924 | 1.0418 | | |
| ### Framework versions | |
| - PEFT 0.8.2 | |
| - Transformers 4.39.0.dev0 | |
| - Pytorch 2.1.2+cu121 | |
| - Datasets 2.17.1 | |
| - Tokenizers 0.15.0 |