Instructions to use Nike-Hanmatheekuna/llama3-8b-sft-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Nike-Hanmatheekuna/llama3-8b-sft-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "Nike-Hanmatheekuna/llama3-8b-sft-qlora") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from Nike-Hanmatheekuna/llama3-8b-sft-qlora: direct link, hf CLI and curl.
- Browser
- Download file 885 Bytes
-
https://huggingface.co/Nike-Hanmatheekuna/llama3-8b-sft-qlora/resolve/main/trainer_state.json
- Command line
-
hf download hf://Nike-Hanmatheekuna/llama3-8b-sft-qlora/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/Nike-Hanmatheekuna/llama3-8b-sft-qlora/resolve/main/trainer_state.json
885 Bytes
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 5.565217391304348, | |
| "eval_steps": 500, | |
| "global_step": 48, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.11594202898550725, | |
| "grad_norm": 1.375, | |
| "learning_rate": 4e-05, | |
| "loss": 2.0688, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 5.565217391304348, | |
| "step": 48, | |
| "total_flos": 7.68376789229568e+16, | |
| "train_loss": 0.8570354779561361, | |
| "train_runtime": 1420.4534, | |
| "train_samples_per_second": 4.663, | |
| "train_steps_per_second": 0.034 | |
| } | |
| ], | |
| "logging_steps": 50, | |
| "max_steps": 48, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 6, | |
| "save_steps": 1000, | |
| "total_flos": 7.68376789229568e+16, | |
| "train_batch_size": 4, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |