Instructions to use selimyagci/pm_task_llama_quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use selimyagci/pm_task_llama_quantized with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("meta-llama/Llama-3.2-1B") model.load_adapter("selimyagci/pm_task_llama_quantized", set_active=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from selimyagci/pm_task_llama_quantized: direct link, hf CLI and curl.
- Browser
- Download file 856 Bytes
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https://huggingface.co/selimyagci/pm_task_llama_quantized/resolve/main/README.md
- Command line
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hf download hf://selimyagci/pm_task_llama_quantized/README.md
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curl -L -o README.md https://huggingface.co/selimyagci/pm_task_llama_quantized/resolve/main/README.md
856 Bytes
| tags: | |
| - adapter-transformers | |
| - llama | |
| # Adapter `selimyagci/pm_task_llama_quantized` for meta-llama/Llama-3.2-1B | |
| An [adapter](https://adapterhub.ml) for the `meta-llama/Llama-3.2-1B` model that was trained on the None dataset. | |
| This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library. | |
| ## Usage | |
| First, install `adapters`: | |
| ``` | |
| pip install -U adapters | |
| ``` | |
| Now, the adapter can be loaded and activated like this: | |
| ```python | |
| from adapters import AutoAdapterModel | |
| model = AutoAdapterModel.from_pretrained("meta-llama/Llama-3.2-1B") | |
| adapter_name = model.load_adapter("selimyagci/pm_task_llama_quantized", set_active=True) | |
| ``` | |
| ## Architecture & Training | |
| <!-- Add some description here --> | |
| ## Evaluation results | |
| <!-- Add some description here --> | |
| ## Citation | |
| <!-- Add some description here --> |