Instructions to use Kaballas/llama_graph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaballas/llama_graph with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Kaballas/llama_graph", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from Kaballas/llama_graph: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/Kaballas/llama_graph/resolve/main/tokenizer.json
- Command line
-
hf download hf://Kaballas/llama_graph/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Kaballas/llama_graph/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 784740a692cdc5769ac0cdc25fb77e65e074187bd2d8e0212b97e699d42c2e30
- Size of remote file:
- 17.2 MB
- SHA256:
- fff3d723694c44ebcab538c089e13694812fa5c2e74d201a9806d66f76e6b6f5
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