Instructions to use vmpsergio/3c613fe3-7c18-4589-887b-fc2df4db04d8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vmpsergio/3c613fe3-7c18-4589-887b-fc2df4db04d8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Mistral-Nemo-Base-2407") model = PeftModel.from_pretrained(base_model, "vmpsergio/3c613fe3-7c18-4589-887b-fc2df4db04d8") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from vmpsergio/3c613fe3-7c18-4589-887b-fc2df4db04d8: direct link, hf CLI and curl.
- Browser
- Download file 456 MB
-
https://huggingface.co/vmpsergio/3c613fe3-7c18-4589-887b-fc2df4db04d8/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://vmpsergio/3c613fe3-7c18-4589-887b-fc2df4db04d8/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/vmpsergio/3c613fe3-7c18-4589-887b-fc2df4db04d8/resolve/main/adapter_model.safetensors
456 MB
- Xet hash:
- bf1b0544ffaacdf00e7b81612ed51f50574c87c8a20d8051c7780c70750ea0ca
- Size of remote file:
- 456 MB
- SHA256:
- 4ff9c24ec3bb17ea444bcb8d3fe687c46c0622cb908638346e8a6417cafa4036
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