Feature Extraction
Transformers
ONNX
Safetensors
French
gemma3_text
text-generation
Summarization
text-embeddings-inference
Instructions to use LugolBis/G3Q-FR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LugolBis/G3Q-FR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LugolBis/G3Q-FR")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LugolBis/G3Q-FR") model = AutoModelForCausalLM.from_pretrained("LugolBis/G3Q-FR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23db2e055d80cccf03598ded35b611c37cce36e1bc0bf93b87209e2a480a073d
- Size of remote file:
- 33.4 MB
- SHA256:
- daab2354f8a74e70d70b4d1f804939b68a8c9624dd06cb7858e52dd8970e9726
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.