Instructions to use gsoaresbaptista/themis-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gsoaresbaptista/themis-instruct with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("dominguesm/canarim-7b-instruct") model = PeftModel.from_pretrained(base_model, "gsoaresbaptista/themis-instruct") - Notebooks
- Google Colab
- Kaggle
themis-instruct
This model is a fine-tuned version of dominguesm/canarim-7b-instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8855
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: 2.5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.0042 | 0.03 | 25 | 1.3872 |
| 1.2414 | 0.05 | 50 | 1.1247 |
| 1.0936 | 0.08 | 75 | 1.0210 |
| 1.0607 | 0.11 | 100 | 0.9881 |
| 1.0186 | 0.13 | 125 | 0.9693 |
| 0.9851 | 0.16 | 150 | 0.9552 |
| 1.0123 | 0.18 | 175 | 0.9469 |
| 0.9737 | 0.21 | 200 | 0.9398 |
| 0.9408 | 0.24 | 225 | 0.9371 |
| 0.9129 | 0.26 | 250 | 0.9300 |
| 0.9441 | 0.29 | 275 | 0.9254 |
| 0.9577 | 0.32 | 300 | 0.9198 |
| 0.9509 | 0.34 | 325 | 0.9162 |
| 0.9023 | 0.37 | 350 | 0.9135 |
| 0.8924 | 0.4 | 375 | 0.9109 |
| 0.9207 | 0.42 | 400 | 0.9086 |
| 0.9436 | 0.45 | 425 | 0.9058 |
| 0.8637 | 0.47 | 450 | 0.9047 |
| 0.9207 | 0.5 | 475 | 0.9033 |
| 0.9475 | 0.53 | 500 | 0.9002 |
| 0.9548 | 0.55 | 525 | 0.8981 |
| 0.8806 | 0.58 | 550 | 0.8969 |
| 0.9475 | 0.61 | 575 | 0.8949 |
| 0.8505 | 0.63 | 600 | 0.8932 |
| 0.8999 | 0.66 | 625 | 0.8926 |
| 0.9018 | 0.68 | 650 | 0.8906 |
| 0.9107 | 0.71 | 675 | 0.8901 |
| 0.8557 | 0.74 | 700 | 0.8888 |
| 0.8903 | 0.76 | 725 | 0.8881 |
| 0.8718 | 0.79 | 750 | 0.8875 |
| 0.9002 | 0.82 | 775 | 0.8870 |
| 0.9086 | 0.84 | 800 | 0.8867 |
| 0.8983 | 0.87 | 825 | 0.8863 |
| 0.9401 | 0.9 | 850 | 0.8861 |
| 0.9434 | 0.92 | 875 | 0.8857 |
| 0.8987 | 0.95 | 900 | 0.8855 |
| 0.9008 | 0.97 | 925 | 0.8855 |
Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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