Instructions to use ugaoo/1213 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ugaoo/1213 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "ugaoo/1213") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: llama3.1 | |
| base_model: meta-llama/Llama-3.1-8B | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - ugaoo/instruction_rawinput_conciseoutput_mimic | |
| model-index: | |
| - name: out/meta_llama_Llama_3.1_8B_ugaoo_instruction_rawinput_conciseoutput_mimic | |
| 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: `0.8.0.dev0` | |
| ```yaml | |
| base_model: meta-llama/Llama-3.1-8B | |
| model_type: AutoModelForCausalLM | |
| tokenizer_type: AutoTokenizer | |
| trust_remote_code: true | |
| load_in_8bit: false | |
| load_in_4bit: true | |
| strict: false | |
| datasets: | |
| - path: ugaoo/instruction_rawinput_conciseoutput_mimic | |
| type: alpaca | |
| val_set_size: 0 | |
| output_dir: ./out/meta_llama_Llama_3.1_8B_ugaoo_instruction_rawinput_conciseoutput_mimic | |
| sequence_len: 4000 | |
| sample_packing: true | |
| pad_to_sequence_len: true | |
| adapter: qlora | |
| lora_r: 256 | |
| lora_alpha: 512 | |
| lora_dropout: 0.05 | |
| lora_target_linear: true | |
| lora_target_modules: | |
| - q_proj | |
| - k_proj | |
| - v_proj | |
| - o_proj | |
| - up_proj | |
| - down_proj | |
| - gate_proj | |
| lora_modules_to_save: | |
| - embed_tokens | |
| - lm_head | |
| wandb_project: cosmosearch | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_name: meta_llama_Llama_3.1_8B_ugaoo_instruction_rawinput_conciseoutput_mimic | |
| wandb_log_model: | |
| gradient_accumulation_steps: 3 | |
| micro_batch_size: 4 | |
| num_epochs: 6 | |
| optimizer: adamw_torch | |
| lr_scheduler: cosine | |
| learning_rate: 5e-6 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: auto | |
| fp16: false | |
| tf32: false | |
| gradient_checkpointing: true | |
| early_stopping_patience: | |
| resume_from_checkpoint: | |
| logging_steps: 1 | |
| xformers_attention: | |
| flash_attention: true | |
| warmup_steps: 100 | |
| evals_per_epoch: 6 | |
| eval_table_size: | |
| saves_per_epoch: 1 | |
| debug: | |
| deepspeed: | |
| weight_decay: 0.0 | |
| fsdp: | |
| fsdp_config: | |
| save_total_limit: 6 | |
| special_tokens: | |
| pad_token: <|end_of_text|> | |
| ``` | |
| </details><br> | |
| # out/meta_llama_Llama_3.1_8B_ugaoo_instruction_rawinput_conciseoutput_mimic | |
| This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the ugaoo/instruction_rawinput_conciseoutput_mimic dataset. | |
| ## 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-06 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - gradient_accumulation_steps: 3 | |
| - total_train_batch_size: 12 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 100 | |
| - num_epochs: 6.0 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.14.0 | |
| - Transformers 4.49.0 | |
| - Pytorch 2.5.1+cu124 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 |