Instructions to use andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B") model = PeftModel.from_pretrained(base_model, "andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora: direct link, hf CLI and curl.
- Browser
- Download file 5.59 kB
-
https://huggingface.co/andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora/resolve/main/training_args.bin
- Command line
-
hf download hf://andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora/resolve/main/training_args.bin
5.59 kB
- Xet hash:
- 27ba609b209369b3b58b5cb53e21c0e3271ef233dc6c4db7493eec81f0937fcb
- Size of remote file:
- 5.59 kB
- SHA256:
- 2ebae3f6d689b27dbd5aec6ae196d2efe427083c82f2a553bc15b3e3e994e0dd
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