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blip2-lora-finetune
viswakiranvvs/blip2-lora-finetune
blip2-lora-finetune is a machine learning model from viswakiranvvs. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Feb 15, 2026
- Updated
- Feb 15, 2026
- SHA
- 159f2de7
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
<a href="https://huggingface.co/spaces/viswakiranvvs/blip2-lora-finetune-trackio" target="_blank"><img src="https://raw.githubusercontent.com/gradio-app/trackio/refs/heads/main/trackio/assets/badge.png" alt="Visualize in Trackio" title="Visualize in Trackio" style="height: 40px;"/></a>
blip2-lora-finetune
This model is a fine-tuned version of Salesforce/blip2-opt-2.7b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8155
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.05
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 83.2389 | 0.5979 | 50 | 10.0021 |
| 18.5524 | 1.1913 | 100 | 1.9922 |
| 9.6493 | 1.7892 | 150 | 1.0606 |
| 8.5570 | 2.3827 | 200 | 0.9584 |
| 7.3530 | 2.9806 | 250 | 0.9102 |
| 8.6532 | 3.5740 | 300 | 0.8919 |
| 6.9255 | 4.1674 | 350 | 0.8629 |
| 7.2563 | 4.7653 | 400 | 0.8558 |
| 7.6426 | 5.3587 | 450 | 0.8349 |
| 7.3618 | 5.9567 | 500 | 0.8488 |
| 7.1585 | 6.5501 | 550 | 0.8302 |
| 8.3336 | 7.1435 | 600 | 0.8298 |
| 6.9589 | 7.7414 | 650 | 0.8193 |
| 7.0150 | 8.3348 | 700 | 0.8237 |
| 7.3567 | 8.9327 | 750 | 0.8238 |
| 8.1129 | 9.5262 | 800 | 0.8183 |
| 6.8732 | 10.1196 | 850 | 0.8128 |
| 6.4406 | 10.7175 | 900 | 0.8155 |
| 6.8474 | 11.3109 | 950 | 0.8236 |
| 6.5659 | 11.9088 | 1000 | 0.8157 |
| 7.1021 | 12.5022 | 1050 | 0.8156 |
| 7.2930 | 13.0957 | 1100 | 0.8174 |
| 6.9147 | 13.6936 | 1150 | 0.8145 |
| 7.0703 | 14.2870 | 1200 | 0.8173 |
| 6.6065 | 14.8849 | 1250 | 0.8155 |
Framework versions
- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2
- Library
- peft
- License
- mit
- Created
- Feb 15, 2026
- Updated
- Feb 15, 2026