Instructions to use joy2000/mistral_instruct_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use joy2000/mistral_instruct_generation with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "joy2000/mistral_instruct_generation") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - generated_from_trainer | |
| base_model: mistralai/Mistral-7B-Instruct-v0.1 | |
| model-index: | |
| - name: mistral_instruct_generation | |
| 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. --> | |
| # mistral_instruct_generation | |
| This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.3338 | |
| ## 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: 1 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: constant | |
| - lr_scheduler_warmup_steps: 0.03 | |
| - training_steps: 100 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.4954 | 0.0 | 20 | 1.3620 | | |
| | 1.4476 | 0.01 | 40 | 1.3493 | | |
| | 1.4787 | 0.01 | 60 | 1.3418 | | |
| | 1.4646 | 0.02 | 80 | 1.3396 | | |
| | 1.4857 | 0.02 | 100 | 1.3338 | | |
| ### Framework versions | |
| - PEFT 0.7.1 | |
| - Transformers 4.36.1 | |
| - Pytorch 2.1.2+cu121 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 |