Instructions to use SaiGaneshanM/Qwen2_5_7B_Python_coding_assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaiGaneshanM/Qwen2_5_7B_Python_coding_assistant with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SaiGaneshanM/Qwen2_5_7B_Python_coding_assistant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SaiGaneshanM/Qwen2_5_7B_Python_coding_assistant: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/SaiGaneshanM/Qwen2_5_7B_Python_coding_assistant/resolve/main/tokenizer.json
- Command line
-
hf download hf://SaiGaneshanM/Qwen2_5_7B_Python_coding_assistant/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SaiGaneshanM/Qwen2_5_7B_Python_coding_assistant/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- a245e2c00e03fb947a7c616790c1e79f18efda727da761dea765be6059b0be38
- Size of remote file:
- 11.4 MB
- SHA256:
- 1815ea80ab714ea1e76500b20468d3bdb4e3a993cf8d473a640a8a44929879e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.