Instructions to use jordip/mistral7b_instruct_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jordip/mistral7b_instruct_generation with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "jordip/mistral7b_instruct_generation") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| datasets: | |
| - generator | |
| base_model: mistralai/Mistral-7B-v0.1 | |
| model-index: | |
| - name: mistral7b_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. --> | |
| # mistral7b_instruct_generation | |
| This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.7925 | |
| ## 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: 4 | |
| - 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: 500 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.9057 | 0.0 | 20 | 1.8347 | | |
| | 1.8154 | 0.01 | 40 | 1.8032 | | |
| | 1.8779 | 0.01 | 60 | 1.7908 | | |
| | 1.9543 | 0.01 | 80 | 1.7954 | | |
| | 1.853 | 0.01 | 100 | 1.7956 | | |
| | 1.8104 | 0.02 | 120 | 1.7903 | | |
| | 1.9193 | 0.02 | 140 | 1.7942 | | |
| | 1.8547 | 0.02 | 160 | 1.7943 | | |
| | 1.858 | 0.03 | 180 | 1.7897 | | |
| | 1.7768 | 0.03 | 200 | 1.7975 | | |
| | 1.8016 | 0.03 | 220 | 1.7935 | | |
| | 1.8096 | 0.03 | 240 | 1.7982 | | |
| | 1.8556 | 0.04 | 260 | 1.7992 | | |
| | 1.927 | 0.04 | 280 | 1.8015 | | |
| | 1.8626 | 0.04 | 300 | 1.7930 | | |
| | 1.943 | 0.04 | 320 | 1.7939 | | |
| | 1.8699 | 0.05 | 340 | 1.7935 | | |
| | 1.8069 | 0.05 | 360 | 1.7944 | | |
| | 1.8291 | 0.05 | 380 | 1.7955 | | |
| | 1.774 | 0.06 | 400 | 1.7886 | | |
| | 1.8625 | 0.06 | 420 | 1.7955 | | |
| | 1.842 | 0.06 | 440 | 1.7961 | | |
| | 1.8625 | 0.06 | 460 | 1.8056 | | |
| | 1.9721 | 0.07 | 480 | 1.7930 | | |
| | 1.7607 | 0.07 | 500 | 1.7925 | | |
| ### Framework versions | |
| - PEFT 0.7.1 | |
| - Transformers 4.37.1 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.1 |