Instructions to use debisoft/Qwen2.5-Coder-7B-Instruct-thinking-function_calling-quant-V0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use debisoft/Qwen2.5-Coder-7B-Instruct-thinking-function_calling-quant-V0 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("debisoft/Qwen2.5-Coder-7B-Instruct-thinking-function_calling-quant-V0", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c88bdae02eefb20b029646661f84f2b5ac1d55b9134ffe65bcd48c4d989f7596
- Size of remote file:
- 5.69 kB
- SHA256:
- cf224a4d3a428e5fc64d224af75f3b3cd47f7ca698c9e5071246e2f623f395f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.