Instructions to use salangarica/run with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use salangarica/run with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("salangarica/BioMistral-LLM") model = PeftModel.from_pretrained(base_model, "salangarica/run") - Notebooks
- Google Colab
- Kaggle
run
This model is a fine-tuned version of salangarica/BioMistral-LLM on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3032
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: 2e-05
- train_batch_size: 8
- 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_ratio: 0.03
- num_epochs: 20.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.131 | 1.0 | 110 | 0.1747 |
| 0.1903 | 2.0 | 220 | 0.1724 |
| 0.0928 | 3.0 | 330 | 0.2107 |
| 0.0738 | 4.0 | 440 | 0.2131 |
| 0.0735 | 5.0 | 550 | 0.3032 |
Framework versions
- PEFT 0.8.2
- Transformers 4.38.1
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.2
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salangarica/BioMistral-LLM