Instructions to use infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Intel/neural-chat-7b-v3-3") model = PeftModel.from_pretrained(base_model, "infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06: direct link, hf CLI and curl.
- Browser
- Download file 671 MB
-
https://huggingface.co/infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06/resolve/main/adapter_model.bin
- Command line
-
hf download hf://infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06/resolve/main/adapter_model.bin
671 MB
- Xet hash:
- 439ba0066734ed9c4274c4f1f8385bf31a9ed6e4534c8326f613016eb6148b8e
- Size of remote file:
- 671 MB
- SHA256:
- 539e575283f71f73ea93043b344c887b3d6d8cb1299445b9a0a785c46cdaed88
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