Instructions to use Tevatron/AgentIR-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tevatron/AgentIR-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Tevatron/AgentIR-4B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Tevatron/AgentIR-4B") model = AutoModel.from_pretrained("Tevatron/AgentIR-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Tevatron/AgentIR-4B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Tevatron/AgentIR-4B/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://Tevatron/AgentIR-4B@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Tevatron/AgentIR-4B/resolve/refs%2Fpr%2F1/tokenizer.json
11.4 MB
- Xet hash:
- 8407b8a5fc62ae108ee9c4cc91f05d5f2bcacedcf3b2e374505ec6a4d9b92c0f
- Size of remote file:
- 11.4 MB
- SHA256:
- 83cdf8c3a34f68862319cb1810ee7b1e2c0a44e0864ae930194ddb76bb7feb8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.