Instructions to use riazmo/out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use riazmo/out with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct") model = PeftModel.from_pretrained(base_model, "riazmo/out") - Notebooks
- Google Colab
- Kaggle
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Download README.md from riazmo/out: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
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https://huggingface.co/riazmo/out/resolve/main/README.md
- Command line
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hf download hf://riazmo/out/README.md
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curl -L -o README.md https://huggingface.co/riazmo/out/resolve/main/README.md
1.25 kB
| library_name: peft | |
| base_model: Qwen/Qwen2.5-VL-3B-Instruct | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: out | |
| 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. --> | |
| # out | |
| This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.05 | |
| - num_epochs: 3 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.14.0 | |
| - Transformers 4.51.3 | |
| - Pytorch 2.4.1+cu124 | |
| - Datasets 5.0.0 | |
| - Tokenizers 0.21.4 |