Text Classification
Transformers
Safetensors
English
deberta-v2
function calling
on-device language model
text-embeddings-inference
Instructions to use squeeze-ai-lab/TinyAgent-ToolRAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use squeeze-ai-lab/TinyAgent-ToolRAG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="squeeze-ai-lab/TinyAgent-ToolRAG")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("squeeze-ai-lab/TinyAgent-ToolRAG") model = AutoModelForSequenceClassification.from_pretrained("squeeze-ai-lab/TinyAgent-ToolRAG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from squeeze-ai-lab/TinyAgent-ToolRAG: direct link, hf CLI and curl.
- Browser
- Download file 8.65 MB
-
https://huggingface.co/squeeze-ai-lab/TinyAgent-ToolRAG/resolve/main/tokenizer.json
- Command line
-
hf download hf://squeeze-ai-lab/TinyAgent-ToolRAG/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/squeeze-ai-lab/TinyAgent-ToolRAG/resolve/main/tokenizer.json
8.65 MB
File too large to display, you can check the raw version instead.