Instructions to use Sreenath/sqlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Sreenath/sqlm with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") model = PeftModel.from_pretrained(base_model, "Sreenath/sqlm") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| base_model: mistralai/Mistral-7B-Instruct-v0.2 | |
| model-index: | |
| - name: sqlm | |
| 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. --> | |
| # sqlm | |
| This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3168 | |
| ## 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: 200 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 0.5858 | 0.0333 | 20 | 0.4605 | | |
| | 0.395 | 0.0667 | 40 | 0.3820 | | |
| | 0.352 | 0.1 | 60 | 0.3551 | | |
| | 0.3695 | 0.1333 | 80 | 0.3410 | | |
| | 0.3281 | 0.1667 | 100 | 0.3359 | | |
| | 0.3304 | 0.2 | 120 | 0.3325 | | |
| | 0.3435 | 0.2333 | 140 | 0.3292 | | |
| | 0.3416 | 0.2667 | 160 | 0.3275 | | |
| | 0.3331 | 0.3 | 180 | 0.3221 | | |
| | 0.328 | 0.3333 | 200 | 0.3168 | | |
| ### Framework versions | |
| - PEFT 0.10.0 | |
| - Transformers 4.40.0 | |
| - Pytorch 2.2.2+cu121 | |
| - Datasets 2.19.0 | |
| - Tokenizers 0.19.1 |