Instructions to use mehmetdavut/ruby3.4-qwen2.5-coder-1.5b-1k-hq-8bit-gemini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mehmetdavut/ruby3.4-qwen2.5-coder-1.5b-1k-hq-8bit-gemini with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "mehmetdavut/ruby3.4-qwen2.5-coder-1.5b-1k-hq-8bit-gemini") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mehmetdavut/ruby3.4-qwen2.5-coder-1.5b-1k-hq-8bit-gemini: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/mehmetdavut/ruby3.4-qwen2.5-coder-1.5b-1k-hq-8bit-gemini/resolve/main/training_args.bin
- Command line
-
hf download hf://mehmetdavut/ruby3.4-qwen2.5-coder-1.5b-1k-hq-8bit-gemini/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mehmetdavut/ruby3.4-qwen2.5-coder-1.5b-1k-hq-8bit-gemini/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 62013c1c757d7f87499db7b98212e0499e6bbc51b7a175be9e3c63943558e3c2
- Size of remote file:
- 5.5 kB
- SHA256:
- 00a1f0cffc5d16badd51d4c271620b99697ac235a7d13d4f9fed1ed2066ff646
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.