Instructions to use mehmetdavut/ruby3.4-qwen2.5-coder-7b-5k-all-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-7b-5k-all-8bit-gemini with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") model = PeftModel.from_pretrained(base_model, "mehmetdavut/ruby3.4-qwen2.5-coder-7b-5k-all-8bit-gemini") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from mehmetdavut/ruby3.4-qwen2.5-coder-7b-5k-all-8bit-gemini: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/mehmetdavut/ruby3.4-qwen2.5-coder-7b-5k-all-8bit-gemini/resolve/main/tokenizer.json
- Command line
-
hf download hf://mehmetdavut/ruby3.4-qwen2.5-coder-7b-5k-all-8bit-gemini/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mehmetdavut/ruby3.4-qwen2.5-coder-7b-5k-all-8bit-gemini/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- b688614557d32d7baa4f3a4019c165dd0520dc4af258a1e6576f477507fd38b4
- Size of remote file:
- 11.4 MB
- SHA256:
- 83396048d512ec1f3178af0d7c1f79a226bba041822614b0e26a4fd2d4b55bf7
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