Text Classification
Transformers
Safetensors
English
qwen2
text-generation
code
qwen
openhusky-coder
text-embeddings-inference
Instructions to use CodeDevX/openhusky-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeDevX/openhusky-coder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CodeDevX/openhusky-coder")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CodeDevX/openhusky-coder") model = AutoModelForCausalLM.from_pretrained("CodeDevX/openhusky-coder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e40809bb0c741c67da3dc1eb69a3309ba6e1b808df77eeb6542ddf3036d2b09
- Size of remote file:
- 932 MB
- SHA256:
- 0d4ccc14777660b36d7309da2c49a4efde8e98b50708c73d6c67336ebcaddc79
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.