Instructions to use Agnij-Moitra/USPTO-50k-DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Agnij-Moitra/USPTO-50k-DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Agnij-Moitra/USPTO-50k-DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit") - Notebooks
- Google Colab
- Kaggle
Upload fine-tuned unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit for USPTO-50k
8e89788 verified - Xet hash:
- ddd1968cb12b5d11c5fcc552d9ca94caca88436f5f848c7b6b7b93abbc7df0c2
- Size of remote file:
- 34.9 MB
- SHA256:
- 26c2a8f26d4e4e033a7004c53276cd12eaaa82fe567688bb1c0028df8494f4f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.