Instructions to use sosuke/train_logs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sosuke/train_logs with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("tokyotech-llm/Swallow-7b-instruct-v0.1") model = PeftModel.from_pretrained(base_model, "sosuke/train_logs") - Notebooks
- Google Colab
- Kaggle
| license: llama2 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - dpo | |
| - generated_from_trainer | |
| base_model: tokyotech-llm/Swallow-7b-instruct-v0.1 | |
| model-index: | |
| - name: train_logs | |
| 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. --> | |
| # train_logs | |
| This model is a fine-tuned version of [tokyotech-llm/Swallow-7b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-7b-instruct-v0.1) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6776 | |
| - Rewards/chosen: 0.1044 | |
| - Rewards/rejected: 0.0678 | |
| - Rewards/accuracies: 0.5983 | |
| - Rewards/margins: 0.0365 | |
| - Logps/rejected: -195.0584 | |
| - Logps/chosen: -198.8751 | |
| - Logits/rejected: -1.2872 | |
| - Logits/chosen: -1.2718 | |
| ## 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 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 8 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - training_steps: 300 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | | |
| |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | |
| | 0.6922 | 0.0351 | 50 | 0.6910 | -0.0173 | -0.0222 | 0.5433 | 0.0050 | -195.9592 | -200.0917 | -1.3115 | -1.2970 | | |
| | 0.6915 | 0.0702 | 100 | 0.6841 | 0.0935 | 0.0721 | 0.5900 | 0.0214 | -195.0160 | -198.9837 | -1.2971 | -1.2823 | | |
| | 0.6819 | 0.1053 | 150 | 0.6792 | 0.1455 | 0.1116 | 0.5900 | 0.0339 | -194.6210 | -198.4638 | -1.2865 | -1.2708 | | |
| | 0.6825 | 0.1404 | 200 | 0.6784 | 0.1161 | 0.0811 | 0.5933 | 0.0350 | -194.9258 | -198.7577 | -1.2871 | -1.2717 | | |
| | 0.6791 | 0.1754 | 250 | 0.6769 | 0.1049 | 0.0670 | 0.6183 | 0.0378 | -195.0665 | -198.8701 | -1.2885 | -1.2730 | | |
| | 0.6826 | 0.2105 | 300 | 0.6776 | 0.1044 | 0.0678 | 0.5983 | 0.0365 | -195.0584 | -198.8751 | -1.2872 | -1.2718 | | |
| ### Framework versions | |
| - PEFT 0.11.1 | |
| - Transformers 4.41.0 | |
| - Pytorch 2.3.0+cu121 | |
| - Datasets 2.19.1 | |
| - Tokenizers 0.19.1 |