Instructions to use Nazneen39/mistral_logical_1k_data_pair with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Nazneen39/mistral_logical_1k_data_pair 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, "Nazneen39/mistral_logical_1k_data_pair") - Notebooks
- Google Colab
- Kaggle
mistral_logical_1k_data_pair
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0473
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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0713 | 0.8889 | 50 | 0.0614 |
| 0.0469 | 1.7644 | 100 | 0.0497 |
| 0.0466 | 2.64 | 150 | 0.0478 |
| 0.0474 | 3.5156 | 200 | 0.0475 |
| 0.0446 | 4.3911 | 250 | 0.0476 |
| 0.0447 | 5.2667 | 300 | 0.0476 |
| 0.0448 | 6.1422 | 350 | 0.0473 |
Framework versions
- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
- 5
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for Nazneen39/mistral_logical_1k_data_pair
Base model
mistralai/Mistral-7B-v0.1