Instructions to use AyaKhaled/phi_vision_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AyaKhaled/phi_vision_checkpoints with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("llava-hf/llava-1.5-7b-hf") model = PeftModel.from_pretrained(base_model, "AyaKhaled/phi_vision_checkpoints") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AyaKhaled/phi_vision_checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
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https://huggingface.co/AyaKhaled/phi_vision_checkpoints/resolve/main/README.md
- Command line
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hf download hf://AyaKhaled/phi_vision_checkpoints/README.md
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curl -L -o README.md https://huggingface.co/AyaKhaled/phi_vision_checkpoints/resolve/main/README.md
1.3 kB
| library_name: peft | |
| license: llama2 | |
| base_model: llava-hf/llava-1.5-7b-hf | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: phi_vision_checkpoints | |
| results: [] | |
| <!-- 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. --> | |
| # phi_vision_checkpoints | |
| This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on an unknown dataset. | |
| ## 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: 0.0002 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 8 | |
| - 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: constant | |
| - lr_scheduler_warmup_ratio: 0.03 | |
| - num_epochs: 2 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.15.1 | |
| - Transformers 4.51.2 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.21.1 |