Instructions to use surrey-nlp/En-Ta_Mono-AG-Llama-2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use surrey-nlp/En-Ta_Mono-AG-Llama-2-13b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf") model = PeftModel.from_pretrained(base_model, "surrey-nlp/En-Ta_Mono-AG-Llama-2-13b") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 1750, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.57, | |
| "grad_norm": 0.6077275276184082, | |
| "learning_rate": 1.948074025402493e-05, | |
| "loss": 0.5215, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "step": 1750, | |
| "total_flos": 3.3565659451244544e+17, | |
| "train_loss": 0.5047183837890625, | |
| "train_runtime": 18395.9977, | |
| "train_samples_per_second": 0.381, | |
| "train_steps_per_second": 0.095 | |
| } | |
| ], | |
| "logging_steps": 1000, | |
| "max_steps": 1750, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 1, | |
| "save_steps": 1000, | |
| "total_flos": 3.3565659451244544e+17, | |
| "train_batch_size": 2, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |