Instructions to use baskarm/deepseek-coder-7b-instruct-v1.5_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use baskarm/deepseek-coder-7b-instruct-v1.5_finetune with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5") model = PeftModel.from_pretrained(base_model, "baskarm/deepseek-coder-7b-instruct-v1.5_finetune") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from baskarm/deepseek-coder-7b-instruct-v1.5_finetune: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/baskarm/deepseek-coder-7b-instruct-v1.5_finetune/resolve/main/training_args.bin
- Command line
-
hf download hf://baskarm/deepseek-coder-7b-instruct-v1.5_finetune/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/baskarm/deepseek-coder-7b-instruct-v1.5_finetune/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- a491a5ba360ed7df31df9300030e833d9fccb32cc1a409e911eccd98b1997335
- Size of remote file:
- 5.24 kB
- SHA256:
- 11cf4789707dd469971638c5ae6391321774f7f58fd32ed8be2b8001dbe0729b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.