# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText
processor = AutoProcessor.from_pretrained("weathon/smiles_llava_ft")
model = AutoModelForImageTextToText.from_pretrained("weathon/smiles_llava_ft")Quick Links
smiles_llava_ft
This model is a fine-tuned version of weathon/smiles_llava on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.0768
- Accuracy: 0.7191
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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.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: 0.05
- num_epochs: 20
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.3041 | 0.9569 | 100 | 3.5557 | 0.0 |
| 2.3241 | 1.9091 | 200 | 2.5052 | 0.1835 |
| 2.029 | 2.8612 | 300 | 2.2936 | 0.5056 |
| 1.9409 | 3.8134 | 400 | 2.2173 | 0.5693 |
| 1.9861 | 4.7656 | 500 | 2.1782 | 0.6030 |
| 1.9564 | 5.7177 | 600 | 2.1461 | 0.6217 |
| 1.9314 | 6.6699 | 700 | 2.1301 | 0.6704 |
| 1.8838 | 7.6220 | 800 | 2.1084 | 0.6854 |
| 1.9538 | 8.5742 | 900 | 2.1052 | 0.7154 |
| 1.8382 | 9.5263 | 1000 | 2.0955 | 0.7191 |
| 1.9399 | 10.4785 | 1100 | 2.1008 | 0.6554 |
| 1.8231 | 11.4306 | 1200 | 2.0939 | 0.6891 |
| 1.8172 | 12.3828 | 1300 | 2.0899 | 0.6929 |
| 1.8708 | 13.3349 | 1400 | 2.0800 | 0.7491 |
| 1.915 | 14.2871 | 1500 | 2.0776 | 0.7116 |
| 1.8387 | 15.2392 | 1600 | 2.0819 | 0.7041 |
| 1.8646 | 16.1914 | 1700 | 2.0771 | 0.7228 |
| 1.7943 | 17.1435 | 1800 | 2.0770 | 0.7041 |
| 1.8878 | 18.0957 | 1900 | 2.0768 | 0.7154 |
| 1.841 | 19.0478 | 2000 | 2.0768 | 0.7191 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Base model
Salesforce/blip-image-captioning-base Finetuned
weathon/smiles_llava
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="weathon/smiles_llava_ft")