Instructions to use apart/llama3.2_1b_instruct_saes_vader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apart/llama3.2_1b_instruct_saes_vader with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("apart/llama3.2_1b_instruct_saes_vader", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Abdullah
Uploading saes for ['model.layers.2', 'model.layers.3', 'model.layers.4', 'model.layers.5', 'model.layers.6', 'model.layers.7', 'model.layers.8', 'model.layers.9', 'model.layers.10', 'model.layers.11', 'model.layers.12', 'model.layers.13', 'model.layers.14']
fb05c99 verified Download model_config.json from apart/llama3.2_1b_instruct_saes_vader: direct link, hf CLI and curl.
- Browser
- Download file 99 Bytes
-
https://huggingface.co/apart/llama3.2_1b_instruct_saes_vader/resolve/main/model_config.json
- Command line
-
hf download hf://apart/llama3.2_1b_instruct_saes_vader/model_config.json
-
curl -L -o model_config.json https://huggingface.co/apart/llama3.2_1b_instruct_saes_vader/resolve/main/model_config.json
99 Bytes
| {"model_name": "meta-llama/Llama-3.2-1B-Instruct", "dataset_name": "lukemarks/vader-post-training"} |