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 last-checkpoint/training_args.bin from infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- 0ce37dfd0359234c4e57c8623fe2ec842dc8558ad8c8d2e66464343e222e3508
- Size of remote file:
- 6.84 kB
- SHA256:
- 67af12a5c24f14eb3a7ba05583ead6ae5e24d5efcc9f5715bd32b95f26cef09c
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