Instructions to use izzcw/dpo_crafting_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use izzcw/dpo_crafting_lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct") model = PeftModel.from_pretrained(base_model, "izzcw/dpo_crafting_lora") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from izzcw/dpo_crafting_lora: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/izzcw/dpo_crafting_lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://izzcw/dpo_crafting_lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/izzcw/dpo_crafting_lora/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 70137f35d02348a0035408d8e0f100db638ca0266aab450b31f8ae6ced3e7b4a
- Size of remote file:
- 17.2 MB
- SHA256:
- 8c1dcab308e7cf5970ea38815e0a62887d705c5b436f869ca27a5dcdd40c36a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.