Instructions to use QinLiuNLP/llama3-sudo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use QinLiuNLP/llama3-sudo 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, "QinLiuNLP/llama3-sudo") - Notebooks
- Google Colab
- Kaggle
| base_model: meta-llama/Meta-Llama-3-8B | |
| datasets: | |
| - HuggingFaceH4/ultrachat_200k | |
| library_name: peft | |
| license: llama3 | |
| tags: | |
| - alignment-handbook | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| model-index: | |
| - name: llama3-sudo | |
| 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. --> | |
| # llama3-sudo | |
| This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the HuggingFaceH4/ultrachat_200k dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0100 | |
| ## 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: 0.0002 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 4 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 256 | |
| - total_eval_batch_size: 16 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 1.3252 | 0.9697 | 24 | 1.1693 | | |
| | 1.1352 | 1.9798 | 49 | 1.0709 | | |
| | 1.1265 | 1.9899 | 98 | 1.0308 | | |
| | 1.1113 | 2.9798 | 147 | 1.0100 | | |
| ### Framework versions | |
| - PEFT 0.12.0 | |
| - Transformers 4.44.0 | |
| - Pytorch 2.1.2 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 |