Text Classification
Transformers
TensorBoard
Safetensors
llama
Generated from Trainer
text-embeddings-inference
Instructions to use rd211/SmolLM2-1.7B-Instruct-RAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rd211/SmolLM2-1.7B-Instruct-RAG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rd211/SmolLM2-1.7B-Instruct-RAG")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rd211/SmolLM2-1.7B-Instruct-RAG") model = AutoModelForSequenceClassification.from_pretrained("rd211/SmolLM2-1.7B-Instruct-RAG") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b29d1301533bf633ce6db539f913480d0ac4fd7c50e44e755807a93f53a07fc8
- Size of remote file:
- 5.37 kB
- SHA256:
- 395509038f96db37a8b9b70d7b34328225858e848abb96d18793ab1a99d9fdc0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.